Fig1.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "d5a5a61f-c533-4a21-8aa6-eb5d8cbae5f3",
   "metadata": {},
   "source": [
    "# Path to Zenodo files folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39598d4e-ca80-4e52-96d0-bc1afe4ab4fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'PATH/TO/ZENODO/FILES/FOLDER'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e719839-7db2-4068-88e5-1f50db207d8d",
   "metadata": {},
   "source": [
    "# Figure 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef0221a8-faa9-46f4-8560-bb8fb12aeda3",
   "metadata": {},
   "source": [
    "## Panel e"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd7660aa-7d65-4664-9890-69a7eb0b7cc5",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ca088008-4f5c-45e3-88b8-a66ce80d180d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define WT sequences\n",
    "\n",
    "proteins = {'P0A9X9': 'CspA',\n",
    "            'P01053': 'CI-2A',\n",
    "            'P06241': 'FYN-SH3'}\n",
    "\n",
    "P0A9X9 = 'ATGACTGGTATTGTCAAATGGTTTAATGCAGATAAAGGTTTTGGTTTTATCACTCCAGATGATGGTTCTAAAGATGTCTTTGTCCATTTTTCTGCAATTCAAAATGATGGTTATAAATCTTTGGATGAAGGTCAAAAAGTCTCTTTCACTATTGAATCTGGTGCAAAAGGTCCAGCAGCAGGTAATGTCACTTCT'\n",
    "P01053 = 'AAAACTGAATGGCCAGAATTGGTTGGTAAATCTGTCGAAGAAGCAAAAAAAGTTATCTTGCAAGATAAACCAGAAGCACAAATCATTGTTTTGCCAGTTGGTACTATTGTTACTATGGAATATAGAATTGATAGAGTCAGATTGTTTGTCGATAAATTGGATAATATCGCACAAGTTCCAAGAGTTGGT'\n",
    "P06241 = 'ACGCTTTTCGTTGCATTGTATGATTATGAAGCAAGAACTGAAGATGATTTGTCTTTCCATAAAGGTGAAAAATTCCAAATCTTGAATTCTTCTGAAGGTGATTGGTGGGAAGCAAGATCTTTGACTACTGGTGAAACTGGTTATATCCCATCTAACTATGTCGCGCCTGTG'\n",
    "\n",
    "prots=[P0A9X9,P01053,P06241]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81efb4b9-127c-47da-9974-bc2b22e2bb07",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/32/dtq6y36s6mjg1zcf2_9dskdm0000gn/T/ipykernel_5966/2687979835.py:5: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  allvariants = pd.read_csv(path+\"Fig1/Sparse_DTS_Cores_fitness_replicates.txt\",\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>Nham_nt</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>Nmut_codons</th>\n",
       "      <th>WT</th>\n",
       "      <th>indel</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>count_e1_s0</th>\n",
       "      <th>...</th>\n",
       "      <th>sigma2_uncorr</th>\n",
       "      <th>fitness</th>\n",
       "      <th>sigma</th>\n",
       "      <th>growthrate1</th>\n",
       "      <th>growthrate1_sigma</th>\n",
       "      <th>growthrate2</th>\n",
       "      <th>growthrate2_sigma</th>\n",
       "      <th>growthrate</th>\n",
       "      <th>growthrate_sigma</th>\n",
       "      <th>Protein</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9873</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TLMVALYDYEARTEDDLSLHKGEKLQVLNSSEGDWWEARSLTTGET...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.149981</td>\n",
       "      <td>0.119072</td>\n",
       "      <td>0.141862</td>\n",
       "      <td>0.239689</td>\n",
       "      <td>0.030180</td>\n",
       "      <td>0.226946</td>\n",
       "      <td>0.009375</td>\n",
       "      <td>0.232834</td>\n",
       "      <td>0.009311</td>\n",
       "      <td>P06241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2746</th>\n",
       "      <td>NaN</td>\n",
       "      <td>KTEWPELVGKSVEEAKKVILQDKPEAQIIVLPVGTIVTMEYRIDRI...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>270</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143105</td>\n",
       "      <td>0.038189</td>\n",
       "      <td>0.118645</td>\n",
       "      <td>0.229667</td>\n",
       "      <td>0.014652</td>\n",
       "      <td>0.222635</td>\n",
       "      <td>0.008945</td>\n",
       "      <td>0.227526</td>\n",
       "      <td>0.007787</td>\n",
       "      <td>P01053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9771</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TLMVALYDYEARTEDDLSFHKGEKLQILNSSEGDWWEARSLTTGET...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.164073</td>\n",
       "      <td>0.010316</td>\n",
       "      <td>0.156208</td>\n",
       "      <td>0.220078</td>\n",
       "      <td>0.035262</td>\n",
       "      <td>0.221325</td>\n",
       "      <td>0.010256</td>\n",
       "      <td>0.225696</td>\n",
       "      <td>0.010252</td>\n",
       "      <td>P06241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>KTEWPELVGKSIEEAKKVILQDKPEAQIIVLPVGTIVTMEYRIDRI...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>103</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151908</td>\n",
       "      <td>0.006908</td>\n",
       "      <td>0.136781</td>\n",
       "      <td>0.213351</td>\n",
       "      <td>0.021711</td>\n",
       "      <td>0.223492</td>\n",
       "      <td>0.009495</td>\n",
       "      <td>0.225473</td>\n",
       "      <td>0.008977</td>\n",
       "      <td>P01053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2869</th>\n",
       "      <td>NaN</td>\n",
       "      <td>KTEWPELVGKSVEEAKKVILQDKPEAQFIVLPVGTIVTMEYRIDRI...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>869</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140544</td>\n",
       "      <td>0.004089</td>\n",
       "      <td>0.102184</td>\n",
       "      <td>0.225144</td>\n",
       "      <td>0.010277</td>\n",
       "      <td>0.222607</td>\n",
       "      <td>0.008785</td>\n",
       "      <td>0.225288</td>\n",
       "      <td>0.006706</td>\n",
       "      <td>P01053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10635</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TLVVALYDYEARTEDDVSFHKGEKMQMLNSSEGDWWEARSLTTGET...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>141</td>\n",
       "      <td>...</td>\n",
       "      <td>0.496796</td>\n",
       "      <td>-4.493979</td>\n",
       "      <td>0.417133</td>\n",
       "      <td>-0.080343</td>\n",
       "      <td>0.053032</td>\n",
       "      <td>-0.061804</td>\n",
       "      <td>0.031054</td>\n",
       "      <td>-0.069925</td>\n",
       "      <td>0.027377</td>\n",
       "      <td>P06241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6480</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TLIVALYDYEARTEDDISIHKGEKVQLLNSSEGDWWEARSLTTGET...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>0.687984</td>\n",
       "      <td>-4.499378</td>\n",
       "      <td>0.518747</td>\n",
       "      <td>-0.066789</td>\n",
       "      <td>0.054532</td>\n",
       "      <td>-0.072341</td>\n",
       "      <td>0.043004</td>\n",
       "      <td>-0.070280</td>\n",
       "      <td>0.034046</td>\n",
       "      <td>P06241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17285</th>\n",
       "      <td>NaN</td>\n",
       "      <td>MTGIIKWFNADKGFGFLTPDDGSKDVFIHFSAIQNDGYKSLDEGQK...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688287</td>\n",
       "      <td>-4.586210</td>\n",
       "      <td>0.688287</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.066794</td>\n",
       "      <td>0.043023</td>\n",
       "      <td>-0.075979</td>\n",
       "      <td>0.045173</td>\n",
       "      <td>P0A9X9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14007</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TLFVALYDYEARTEDDVSVHKGEKLQFLNSSEGDWWEARSLTTGET...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688254</td>\n",
       "      <td>-4.595361</td>\n",
       "      <td>0.688254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.067366</td>\n",
       "      <td>0.043021</td>\n",
       "      <td>-0.076579</td>\n",
       "      <td>0.045171</td>\n",
       "      <td>P06241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7201</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TLIVALYDYEARTEDDVSMHKGEKFQLLNSSEGDWWEARSLTTGET...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>0.687721</td>\n",
       "      <td>-4.766893</td>\n",
       "      <td>0.687721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.078088</td>\n",
       "      <td>0.042988</td>\n",
       "      <td>-0.087837</td>\n",
       "      <td>0.045136</td>\n",
       "      <td>P06241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24935 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       nt_seq                                             aa_seq  Nham_nt  \\\n",
       "9873      NaN  TLMVALYDYEARTEDDLSLHKGEKLQVLNSSEGDWWEARSLTTGET...      NaN   \n",
       "2746      NaN  KTEWPELVGKSVEEAKKVILQDKPEAQIIVLPVGTIVTMEYRIDRI...      NaN   \n",
       "9771      NaN  TLMVALYDYEARTEDDLSFHKGEKLQILNSSEGDWWEARSLTTGET...      NaN   \n",
       "0         NaN  KTEWPELVGKSIEEAKKVILQDKPEAQIIVLPVGTIVTMEYRIDRI...      NaN   \n",
       "2869      NaN  KTEWPELVGKSVEEAKKVILQDKPEAQFIVLPVGTIVTMEYRIDRI...      NaN   \n",
       "...       ...                                                ...      ...   \n",
       "10635     NaN  TLVVALYDYEARTEDDVSFHKGEKMQMLNSSEGDWWEARSLTTGET...      NaN   \n",
       "6480      NaN  TLIVALYDYEARTEDDISIHKGEKVQLLNSSEGDWWEARSLTTGET...      NaN   \n",
       "17285     NaN  MTGIIKWFNADKGFGFLTPDDGSKDVFIHFSAIQNDGYKSLDEGQK...      NaN   \n",
       "14007     NaN  TLFVALYDYEARTEDDVSVHKGEKLQFLNSSEGDWWEARSLTTGET...      NaN   \n",
       "7201      NaN  TLIVALYDYEARTEDDVSMHKGEKFQLLNSSEGDWWEARSLTTGET...      NaN   \n",
       "\n",
       "       Nham_aa  Nmut_codons    WT  indel   STOP  STOP_readthrough  \\\n",
       "9873       NaN          NaN   NaN   True  False             False   \n",
       "2746       2.0          NaN   NaN  False  False             False   \n",
       "9771       NaN          NaN   NaN   True  False             False   \n",
       "0          3.0          NaN   NaN  False  False             False   \n",
       "2869       0.0          NaN  True  False  False             False   \n",
       "...        ...          ...   ...    ...    ...               ...   \n",
       "10635      NaN          NaN   NaN   True  False             False   \n",
       "6480       NaN          NaN   NaN   True  False             False   \n",
       "17285      NaN          NaN   NaN   True  False             False   \n",
       "14007      NaN          NaN   NaN   True  False             False   \n",
       "7201       NaN          NaN   NaN   True  False             False   \n",
       "\n",
       "       count_e1_s0  ...  sigma2_uncorr   fitness     sigma  growthrate1  \\\n",
       "9873            50  ...       0.149981  0.119072  0.141862     0.239689   \n",
       "2746           270  ...       0.143105  0.038189  0.118645     0.229667   \n",
       "9771            36  ...       0.164073  0.010316  0.156208     0.220078   \n",
       "0              103  ...       0.151908  0.006908  0.136781     0.213351   \n",
       "2869           869  ...       0.140544  0.004089  0.102184     0.225144   \n",
       "...            ...  ...            ...       ...       ...          ...   \n",
       "10635          141  ...       0.496796 -4.493979  0.417133    -0.080343   \n",
       "6480            92  ...       0.687984 -4.499378  0.518747    -0.066789   \n",
       "17285           66  ...       0.688287 -4.586210  0.688287          NaN   \n",
       "14007           73  ...       0.688254 -4.595361  0.688254          NaN   \n",
       "7201            82  ...       0.687721 -4.766893  0.687721          NaN   \n",
       "\n",
       "       growthrate1_sigma  growthrate2  growthrate2_sigma  growthrate  \\\n",
       "9873            0.030180     0.226946           0.009375    0.232834   \n",
       "2746            0.014652     0.222635           0.008945    0.227526   \n",
       "9771            0.035262     0.221325           0.010256    0.225696   \n",
       "0               0.021711     0.223492           0.009495    0.225473   \n",
       "2869            0.010277     0.222607           0.008785    0.225288   \n",
       "...                  ...          ...                ...         ...   \n",
       "10635           0.053032    -0.061804           0.031054   -0.069925   \n",
       "6480            0.054532    -0.072341           0.043004   -0.070280   \n",
       "17285                NaN    -0.066794           0.043023   -0.075979   \n",
       "14007                NaN    -0.067366           0.043021   -0.076579   \n",
       "7201                 NaN    -0.078088           0.042988   -0.087837   \n",
       "\n",
       "       growthrate_sigma  Protein  \n",
       "9873           0.009311   P06241  \n",
       "2746           0.007787   P01053  \n",
       "9771           0.010252   P06241  \n",
       "0              0.008977   P01053  \n",
       "2869           0.006706   P01053  \n",
       "...                 ...      ...  \n",
       "10635          0.027377   P06241  \n",
       "6480           0.034046   P06241  \n",
       "17285          0.045173   P0A9X9  \n",
       "14007          0.045171   P06241  \n",
       "7201           0.045136   P06241  \n",
       "\n",
       "[24935 rows x 27 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "allvariants = pd.read_csv(path+\"Fig1/Sparse_DTS_Cores_fitness_replicates.txt\",\n",
    "                         sep='\\t', lineterminator='\\n', low_memory = False)\n",
    "\n",
    "allvariants = allvariants.sort_values('fitness', ascending = False)\n",
    "\n",
    "# De-multiplex each protein's variants\n",
    "\n",
    "ID_dict =  {'MT' : 'P0A9X9',\n",
    "           'KT' : 'P01053',\n",
    "           'TL' : 'P06241'}\n",
    "\n",
    "for index, row in allvariants.iterrows():\n",
    "    try:\n",
    "        allvariants.at[index,'Protein']=ID_dict[row.aa_seq[:2]]\n",
    "    except KeyError:\n",
    "        continue\n",
    "\n",
    "allvariants = allvariants.loc[allvariants['Protein'].notnull()].copy()\n",
    "allvariants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a4d54d79-b79f-4bf3-88bd-07ad1dac1391",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find core sequences\n",
    "\n",
    "cores = {'P0A9X9' : [4,16,25,27,46,48,62],\n",
    "        'P01053' : [11,18,27,45,47,49,55],\n",
    "        'P06241' : [2,16,18,24,26,48,53]}\n",
    "\n",
    "for index, row in allvariants.iterrows():\n",
    "    core=[]\n",
    "    for i in cores[row.Protein]:\n",
    "        core.append(row.aa_seq[i])\n",
    "    allvariants.at[index,'core'] = ''.join(core)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a36a5ce5-4cca-4825-89c9-9c0e51745bd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'P0A9X9': 'VIVVVFV', 'P01053': 'VIIVLVI', 'P06241': 'FLFFIIV'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define WT cores\n",
    "\n",
    "from Bio.Seq import Seq\n",
    "\n",
    "wt_cores = {}\n",
    "for (prot_seq, prot_nam) in zip(prots, proteins.keys()):\n",
    "    aa_seq=str(Seq(prot_seq).translate())\n",
    "    wt_core=[]\n",
    "    for i in cores[prot_nam]:\n",
    "        wt_core.append(aa_seq[i])\n",
    "    wt_cores[prot_nam]= ''.join(wt_core)\n",
    "wt_cores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "939d3557-c1b7-47f9-8cbd-e19f75af21eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nt_seq</th>\n",
       "      <th>aa_seq</th>\n",
       "      <th>Nham_nt</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>Nmut_codons</th>\n",
       "      <th>WT</th>\n",
       "      <th>indel</th>\n",
       "      <th>STOP</th>\n",
       "      <th>STOP_readthrough</th>\n",
       "      <th>count_e1_s0</th>\n",
       "      <th>...</th>\n",
       "      <th>sigma</th>\n",
       "      <th>growthrate1</th>\n",
       "      <th>growthrate1_sigma</th>\n",
       "      <th>growthrate2</th>\n",
       "      <th>growthrate2_sigma</th>\n",
       "      <th>growthrate</th>\n",
       "      <th>growthrate_sigma</th>\n",
       "      <th>Protein</th>\n",
       "      <th>core</th>\n",
       "      <th>wt_core</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9873</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TLMVALYDYEARTEDDLSLHKGEKLQVLNSSEGDWWEARSLTTGET...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.141862</td>\n",
       "      <td>0.239689</td>\n",
       "      <td>0.030180</td>\n",
       "      <td>0.226946</td>\n",
       "      <td>0.009375</td>\n",
       "      <td>0.232834</td>\n",
       "      <td>0.009311</td>\n",
       "      <td>P06241</td>\n",
       "      <td>MLLLVVL</td>\n",
       "      <td>FLFFIIV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2746</th>\n",
       "      <td>NaN</td>\n",
       "      <td>KTEWPELVGKSVEEAKKVILQDKPEAQIIVLPVGTIVTMEYRIDRI...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>270</td>\n",
       "      <td>...</td>\n",
       "      <td>0.118645</td>\n",
       "      <td>0.229667</td>\n",
       "      <td>0.014652</td>\n",
       "      <td>0.222635</td>\n",
       "      <td>0.008945</td>\n",
       "      <td>0.227526</td>\n",
       "      <td>0.007787</td>\n",
       "      <td>P01053</td>\n",
       "      <td>VIIIVVV</td>\n",
       "      <td>VIIVLVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9771</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TLMVALYDYEARTEDDLSFHKGEKLQILNSSEGDWWEARSLTTGET...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.156208</td>\n",
       "      <td>0.220078</td>\n",
       "      <td>0.035262</td>\n",
       "      <td>0.221325</td>\n",
       "      <td>0.010256</td>\n",
       "      <td>0.225696</td>\n",
       "      <td>0.010252</td>\n",
       "      <td>P06241</td>\n",
       "      <td>MLFLIVI</td>\n",
       "      <td>FLFFIIV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>KTEWPELVGKSIEEAKKVILQDKPEAQIIVLPVGTIVTMEYRIDRI...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>103</td>\n",
       "      <td>...</td>\n",
       "      <td>0.136781</td>\n",
       "      <td>0.213351</td>\n",
       "      <td>0.021711</td>\n",
       "      <td>0.223492</td>\n",
       "      <td>0.009495</td>\n",
       "      <td>0.225473</td>\n",
       "      <td>0.008977</td>\n",
       "      <td>P01053</td>\n",
       "      <td>IIIIVVV</td>\n",
       "      <td>VIIVLVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2869</th>\n",
       "      <td>NaN</td>\n",
       "      <td>KTEWPELVGKSVEEAKKVILQDKPEAQFIVLPVGTIVTMEYRIDRI...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>869</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102184</td>\n",
       "      <td>0.225144</td>\n",
       "      <td>0.010277</td>\n",
       "      <td>0.222607</td>\n",
       "      <td>0.008785</td>\n",
       "      <td>0.225288</td>\n",
       "      <td>0.006706</td>\n",
       "      <td>P01053</td>\n",
       "      <td>VIFIIVV</td>\n",
       "      <td>VIIVLVI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10635</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TLVVALYDYEARTEDDVSFHKGEKMQMLNSSEGDWWEARSLTTGET...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>141</td>\n",
       "      <td>...</td>\n",
       "      <td>0.417133</td>\n",
       "      <td>-0.080343</td>\n",
       "      <td>0.053032</td>\n",
       "      <td>-0.061804</td>\n",
       "      <td>0.031054</td>\n",
       "      <td>-0.069925</td>\n",
       "      <td>0.027377</td>\n",
       "      <td>P06241</td>\n",
       "      <td>VVFMMML</td>\n",
       "      <td>FLFFIIV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6480</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TLIVALYDYEARTEDDISIHKGEKVQLLNSSEGDWWEARSLTTGET...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>0.518747</td>\n",
       "      <td>-0.066789</td>\n",
       "      <td>0.054532</td>\n",
       "      <td>-0.072341</td>\n",
       "      <td>0.043004</td>\n",
       "      <td>-0.070280</td>\n",
       "      <td>0.034046</td>\n",
       "      <td>P06241</td>\n",
       "      <td>IIIVLVL</td>\n",
       "      <td>FLFFIIV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17285</th>\n",
       "      <td>NaN</td>\n",
       "      <td>MTGIIKWFNADKGFGFLTPDDGSKDVFIHFSAIQNDGYKSLDEGQK...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688287</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.066794</td>\n",
       "      <td>0.043023</td>\n",
       "      <td>-0.075979</td>\n",
       "      <td>0.045173</td>\n",
       "      <td>P0A9X9</td>\n",
       "      <td>ILVILFF</td>\n",
       "      <td>VIVVVFV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14007</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TLFVALYDYEARTEDDVSVHKGEKLQFLNSSEGDWWEARSLTTGET...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.067366</td>\n",
       "      <td>0.043021</td>\n",
       "      <td>-0.076579</td>\n",
       "      <td>0.045171</td>\n",
       "      <td>P06241</td>\n",
       "      <td>FVVLFMM</td>\n",
       "      <td>FLFFIIV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7201</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TLIVALYDYEARTEDDVSMHKGEKFQLLNSSEGDWWEARSLTTGET...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>0.687721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.078088</td>\n",
       "      <td>0.042988</td>\n",
       "      <td>-0.087837</td>\n",
       "      <td>0.045136</td>\n",
       "      <td>P06241</td>\n",
       "      <td>IVMFLMV</td>\n",
       "      <td>FLFFIIV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24935 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       nt_seq                                             aa_seq  Nham_nt  \\\n",
       "9873      NaN  TLMVALYDYEARTEDDLSLHKGEKLQVLNSSEGDWWEARSLTTGET...      NaN   \n",
       "2746      NaN  KTEWPELVGKSVEEAKKVILQDKPEAQIIVLPVGTIVTMEYRIDRI...      NaN   \n",
       "9771      NaN  TLMVALYDYEARTEDDLSFHKGEKLQILNSSEGDWWEARSLTTGET...      NaN   \n",
       "0         NaN  KTEWPELVGKSIEEAKKVILQDKPEAQIIVLPVGTIVTMEYRIDRI...      NaN   \n",
       "2869      NaN  KTEWPELVGKSVEEAKKVILQDKPEAQFIVLPVGTIVTMEYRIDRI...      NaN   \n",
       "...       ...                                                ...      ...   \n",
       "10635     NaN  TLVVALYDYEARTEDDVSFHKGEKMQMLNSSEGDWWEARSLTTGET...      NaN   \n",
       "6480      NaN  TLIVALYDYEARTEDDISIHKGEKVQLLNSSEGDWWEARSLTTGET...      NaN   \n",
       "17285     NaN  MTGIIKWFNADKGFGFLTPDDGSKDVFIHFSAIQNDGYKSLDEGQK...      NaN   \n",
       "14007     NaN  TLFVALYDYEARTEDDVSVHKGEKLQFLNSSEGDWWEARSLTTGET...      NaN   \n",
       "7201      NaN  TLIVALYDYEARTEDDVSMHKGEKFQLLNSSEGDWWEARSLTTGET...      NaN   \n",
       "\n",
       "       Nham_aa  Nmut_codons    WT  indel   STOP  STOP_readthrough  \\\n",
       "9873       6.0          NaN   NaN   True  False             False   \n",
       "2746       3.0          NaN   NaN  False  False             False   \n",
       "9771       4.0          NaN   NaN   True  False             False   \n",
       "0          4.0          NaN   NaN  False  False             False   \n",
       "2869       4.0          NaN  True  False  False             False   \n",
       "...        ...          ...   ...    ...    ...               ...   \n",
       "10635      6.0          NaN   NaN   True  False             False   \n",
       "6480       7.0          NaN   NaN   True  False             False   \n",
       "17285      5.0          NaN   NaN   True  False             False   \n",
       "14007      6.0          NaN   NaN   True  False             False   \n",
       "7201       5.0          NaN   NaN   True  False             False   \n",
       "\n",
       "       count_e1_s0  ...     sigma  growthrate1  growthrate1_sigma  \\\n",
       "9873            50  ...  0.141862     0.239689           0.030180   \n",
       "2746           270  ...  0.118645     0.229667           0.014652   \n",
       "9771            36  ...  0.156208     0.220078           0.035262   \n",
       "0              103  ...  0.136781     0.213351           0.021711   \n",
       "2869           869  ...  0.102184     0.225144           0.010277   \n",
       "...            ...  ...       ...          ...                ...   \n",
       "10635          141  ...  0.417133    -0.080343           0.053032   \n",
       "6480            92  ...  0.518747    -0.066789           0.054532   \n",
       "17285           66  ...  0.688287          NaN                NaN   \n",
       "14007           73  ...  0.688254          NaN                NaN   \n",
       "7201            82  ...  0.687721          NaN                NaN   \n",
       "\n",
       "       growthrate2  growthrate2_sigma  growthrate  growthrate_sigma  Protein  \\\n",
       "9873      0.226946           0.009375    0.232834          0.009311   P06241   \n",
       "2746      0.222635           0.008945    0.227526          0.007787   P01053   \n",
       "9771      0.221325           0.010256    0.225696          0.010252   P06241   \n",
       "0         0.223492           0.009495    0.225473          0.008977   P01053   \n",
       "2869      0.222607           0.008785    0.225288          0.006706   P01053   \n",
       "...            ...                ...         ...               ...      ...   \n",
       "10635    -0.061804           0.031054   -0.069925          0.027377   P06241   \n",
       "6480     -0.072341           0.043004   -0.070280          0.034046   P06241   \n",
       "17285    -0.066794           0.043023   -0.075979          0.045173   P0A9X9   \n",
       "14007    -0.067366           0.043021   -0.076579          0.045171   P06241   \n",
       "7201     -0.078088           0.042988   -0.087837          0.045136   P06241   \n",
       "\n",
       "          core  wt_core  \n",
       "9873   MLLLVVL  FLFFIIV  \n",
       "2746   VIIIVVV  VIIVLVI  \n",
       "9771   MLFLIVI  FLFFIIV  \n",
       "0      IIIIVVV  VIIVLVI  \n",
       "2869   VIFIIVV  VIIVLVI  \n",
       "...        ...      ...  \n",
       "10635  VVFMMML  FLFFIIV  \n",
       "6480   IIIVLVL  FLFFIIV  \n",
       "17285  ILVILFF  VIVVVFV  \n",
       "14007  FVVLFMM  FLFFIIV  \n",
       "7201   IVMFLMV  FLFFIIV  \n",
       "\n",
       "[24935 rows x 29 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find Hamming distance to WT\n",
    "\n",
    "for index, row in allvariants.iterrows():\n",
    "    wt = wt_cores[row.Protein]\n",
    "    allvariants.at[index, 'wt_core']=wt\n",
    "    if row.core == wt:\n",
    "        allvariants.at[index, 'Nham_aa'] = 0\n",
    "    else:\n",
    "        allvariants.at[index, 'Nham_aa'] = sum(1 for a,b in zip(wt, row.core) if a!=b)\n",
    "allvariants"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177a690f-8a81-4e10-8511-428124c4e9de",
   "metadata": {},
   "source": [
    "### Biological replicate correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "113470af-b3e2-4618-9cd6-cee8f996c33d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 120x120 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 120x120 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 120x120 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from seaborn import kdeplot\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "def reg_coef(x,y,label=None,color=None,hue=None,**kwargs):\n",
    "    ax = plt.gca()\n",
    "    r,p = pearsonr(x,y)\n",
    "    ax.annotate('{:.2f}'.format(r), xy=(0.5,0.5), xycoords='axes fraction', ha='center', size = 8, color='red')\n",
    "    ax.tick_params(right=False, top=False, left=False, bottom=False)\n",
    "    ax.spines['bottom'].set_visible(True)\n",
    "    ax.spines['left'].set_visible(True)\n",
    "    ax.spines['top'].set_visible(True)\n",
    "    ax.spines['right'].set_visible(True)\n",
    "    #ax.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n",
    "def replicate(x,**kwargs):\n",
    "    ax = plt.gca()\n",
    "    ax.text(.5,.5,'Rep.'+x.name[7:8],size=8,ha='center',va='center', transform=ax.transAxes)\n",
    "    ax.set_axis_off()\n",
    "    \n",
    "def hexbin(x, y, color, **kwargs): #https://stackoverflow.com/questions/40495093/hexbin-plot-in-pairgrid-with-seaborn\n",
    "    ax = plt.gca()\n",
    "    cmap = sns.color_palette('viridis', as_cmap=True)\n",
    "    ax.hexbin(x, y, bins='log',gridsize=50, cmap=cmap, extent=[min(x), max(x), min(y), max(y)], **kwargs, rasterized=True)\n",
    "    #lims = np.min([ax.get_xlim(), ax.get_ylim()]), np.max([ax.get_xlim(), ax.get_ylim()])\n",
    "    lims=(-2,0.5,-2,0.5)\n",
    "    plt.plot(lims, lims, '--', color='k', linewidth=0.3)\n",
    "    ax.spines['bottom'].set_visible(True)\n",
    "    ax.spines['left'].set_visible(True)\n",
    "    ax.spines['top'].set_visible(True)\n",
    "    ax.spines['right'].set_visible(True)\n",
    "    ax.set_ylim(-1.6,0.6)\n",
    "    ax.set_xlim(-1.6,0.6)\n",
    "    ax.set_xticks(np.arange(-1.5,1,0.5),labels=np.arange(-1.5,1,0.5),rotation=90)\n",
    "    ax.set_yticks(np.arange(-1.5,1,0.5))\n",
    "    ax.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "def normalize(values, bounds):\n",
    "    return [bounds['desired']['lower'] + (x - bounds['actual']['lower']) * (bounds['desired']['upper'] - bounds['desired']['lower']) / (bounds['actual']['upper'] - bounds['actual']['lower']) for x in values]\n",
    "\n",
    "for protein in ['P06241','P01053','P0A9X9']:\n",
    "    \n",
    "    protein_variants = allvariants.loc[allvariants['Protein']==protein].copy()\n",
    "\n",
    "    # Normalize data in the -1 to 0 range\n",
    "    reference=allvariants.loc[allvariants['Protein']==protein].sort_values(['Nham_aa','fitness'], ascending=[True,False]).iloc[0]['core']\n",
    "    fig = plt.figure(figsize=(0,0))\n",
    "    lines = kdeplot(data=protein_variants,x='fitness',linewidth=0).get_lines()\n",
    "    for line in lines:\n",
    "        x, y = line.get_data()\n",
    "        mode=(x[np.argmax(y)])\n",
    "    plt.close()\n",
    "        \n",
    "    protein_variants_scaled = protein_variants.copy()    \n",
    "    \n",
    "    protein_variants_scaled['fitness']=normalize(list(protein_variants_scaled['fitness']),{'actual': {'lower': mode, 'upper': protein_variants_scaled.loc[protein_variants_scaled['core']==reference]['fitness'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "    protein_variants_scaled['fitness1_uncorr']=normalize(list(protein_variants_scaled['fitness1_uncorr']),{'actual': {'lower': mode, 'upper': protein_variants_scaled.loc[protein_variants_scaled['core']==reference]['fitness1_uncorr'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "    protein_variants_scaled['fitness2_uncorr']=normalize(list(protein_variants_scaled['fitness2_uncorr']),{'actual': {'lower': mode, 'upper': protein_variants_scaled.loc[protein_variants_scaled['core']==reference]['fitness2_uncorr'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "    \n",
    "    g = sns.PairGrid(protein_variants_scaled[['fitness1_uncorr', 'fitness2_uncorr', 'Protein']].dropna())\n",
    "    g.map_upper(reg_coef, hue=None)\n",
    "    g.map_diag(replicate)\n",
    "    g.map_lower(hexbin)\n",
    "    g.fig.set_size_inches(1.2,1.2)\n",
    "    \n",
    "    for i in range(2):\n",
    "        g.axes[i, i].set_visible(False)\n",
    "    \n",
    "    for ax in g.axes.flatten():\n",
    "        if ax.get_xlabel() != '':\n",
    "            if str(ax.get_xlabel().split('_')[0][-1]) == '1':\n",
    "                ax.set_xlabel('Abundance fitness', fontsize=7)\n",
    "        if ax.get_ylabel() != '':\n",
    "            if str(ax.get_ylabel().split('_')[0][-1]) == '1' or str(ax.get_ylabel().split('_')[0][-1]) == '3':\n",
    "                ax.set_ylabel('')\n",
    "            else:\n",
    "                ax.set_ylabel('Abundance fitness', fontsize=7)\n",
    "    plt.rcParams['svg.fonttype'] = 'none'\n",
    "    #plt.savefig(protein+'_biol_rep_correl.svg', dpi=300, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a8bfd6-adbf-4659-b521-9781398dbb17",
   "metadata": {},
   "source": [
    "### Fitness landscapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "218bcebf-1fb9-4a0a-be90-9661ebe53fcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import AxesGrid\n",
    "\n",
    "def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1, name='shiftedcmap'):\n",
    "    '''\n",
    "    Function to offset the \"center\" of a colormap. Useful for\n",
    "    data with a negative min and positive max and you want the\n",
    "    middle of the colormap's dynamic range to be at zero.\n",
    "\n",
    "    Input\n",
    "    -----\n",
    "      cmap : The matplotlib colormap to be altered\n",
    "      start : Offset from lowest point in the colormap's range.\n",
    "          Defaults to 0.0 (no lower offset). Should be between\n",
    "          0.0 and `midpoint`.\n",
    "      midpoint : The new center of the colormap. Defaults to \n",
    "          0.5 (no shift). Should be between 0.0 and 1.0. In\n",
    "          general, this should be  1 - vmax / (vmax + abs(vmin))\n",
    "          For example if your data range from -15.0 to +5.0 and\n",
    "          you want the center of the colormap at 0.0, `midpoint`\n",
    "          should be set to  1 - 5/(5 + 15)) or 0.75\n",
    "      stop : Offset from highest point in the colormap's range.\n",
    "          Defaults to 1.0 (no upper offset). Should be between\n",
    "          `midpoint` and 1.0.\n",
    "    '''\n",
    "    cdict = {\n",
    "        'red': [],\n",
    "        'green': [],\n",
    "        'blue': [],\n",
    "        'alpha': []\n",
    "    }\n",
    "\n",
    "    # regular index to compute the colors\n",
    "    reg_index = np.linspace(start, stop, 257)\n",
    "\n",
    "    # shifted index to match the data\n",
    "    shift_index = np.hstack([\n",
    "        np.linspace(0.0, midpoint, 128, endpoint=False), \n",
    "        np.linspace(midpoint, 1.0, 129, endpoint=True)\n",
    "    ])\n",
    "\n",
    "    for ri, si in zip(reg_index, shift_index):\n",
    "        r, g, b, a = cmap(ri)\n",
    "\n",
    "        cdict['red'].append((si, r, r))\n",
    "        cdict['green'].append((si, g, g))\n",
    "        cdict['blue'].append((si, b, b))\n",
    "        cdict['alpha'].append((si, a, a))\n",
    "\n",
    "    newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict)\n",
    "    plt.register_cmap(cmap=newcmap)\n",
    "\n",
    "    return newcmap\n",
    "iterator_cmap = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8147925-b82d-495d-ada0-fecc09654370",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 78125/78125 [00:01<00:00, 56668.13it/s]\n"
     ]
    }
   ],
   "source": [
    "# Define all variants separated by 1 mutation\n",
    "\n",
    "from tqdm import tqdm\n",
    "from itertools import product\n",
    "\n",
    "DTS = ['F','L','I','M','V']\n",
    "a=list(product(DTS,repeat=7))\n",
    "\n",
    "\n",
    "\n",
    "dod={}\n",
    "for variant in tqdm(a, total=len(a)):\n",
    "    iterator=0\n",
    "    positerator=0\n",
    "    singles=[]\n",
    "    for i in product(variant,DTS):\n",
    "        mutant = ''.join(''.join(variant[:positerator])+str(i[1])+''.join(variant[positerator+1:]))\n",
    "        if (iterator/5).is_integer() == True and iterator !=0:\n",
    "            positerator+=1\n",
    "        iterator+=1\n",
    "        if mutant != ''.join(variant):\n",
    "            singles.append(mutant)\n",
    "    dod[''.join(variant)]=singles\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61c6dc93-daff-45e5-804f-0b51746a3371",
   "metadata": {},
   "source": [
    "#### P06241 - FYN-SH3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a714e487-8e2b-43e5-a22d-9221afb03a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 230x250 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 230x250 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 230x250 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Arbitrary threshold at fitness = -0.5\n",
    "\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "from itertools import count\n",
    "from seaborn import kdeplot\n",
    "from itertools import groupby\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "\n",
    "validated_variants = {'P06241': ['FLFFVIV', 'MLFFIVL', 'MFFFVIL', 'MMFLIVF', 'LMFMLVF', 'LMVLLVL'],\n",
    "                     'P01053': ['VIVVLVI', 'VIIIVVV', 'IIVIVVV', 'LMFIVVV', 'ILVIVIV'],\n",
    "                     'P0A9X9': []}\n",
    "\n",
    "protein_colors = {'P06241': '#B4D689',\n",
    "                 'P01053': '#F6DCC9',\n",
    "                 'P0A9X9': '#B1D9D8'}\n",
    "\n",
    "total_core_viabilities = pd.DataFrame(columns=['protein', 'variants_above_01', 'variants_above_025', 'variants_above_05', 'variants_below_05'])\n",
    "\n",
    "for protein in ['P06241','P01053','P0A9X9']:\n",
    "    \n",
    "    protein_variants = allvariants.loc[allvariants['Protein']==protein].copy()\n",
    "\n",
    "    # Normalize data in the -1 to 0 range\n",
    "    reference=allvariants.loc[allvariants['Protein']==protein].sort_values(['Nham_aa','fitness'], ascending=[True,False]).iloc[0]['core']\n",
    "    \n",
    "    fig = plt.figure(figsize=(0,0))\n",
    "    lines = kdeplot(data=protein_variants,x='fitness',linewidth=0).get_lines()\n",
    "    for line in lines:\n",
    "        x, y = line.get_data()\n",
    "        mode=(x[np.argmax(y)])\n",
    "        ax.axvline(x[np.argmax(y)], ls='--')\n",
    "    plt.close()\n",
    "        \n",
    "    protein_variants_scaled = protein_variants.copy()    \n",
    "    \n",
    "    protein_variants_scaled['fitness']=normalize(list(protein_variants_scaled['fitness']),{'actual': {'lower': mode, 'upper': protein_variants_scaled.loc[protein_variants_scaled['core']==reference]['fitness'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "    protein_variants_scaled['fitness1_uncorr']=normalize(list(protein_variants_scaled['fitness1_uncorr']),{'actual': {'lower': mode, 'upper': protein_variants_scaled.loc[protein_variants_scaled['core']==reference]['fitness1_uncorr'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "    protein_variants_scaled['fitness2_uncorr']=normalize(list(protein_variants_scaled['fitness2_uncorr']),{'actual': {'lower': mode, 'upper': protein_variants_scaled.loc[protein_variants_scaled['core']==reference]['fitness2_uncorr'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "\n",
    "    lowest_nham = int(protein_variants_scaled.loc[protein_variants_scaled['core']==reference].iloc[0]['Nham_aa'])\n",
    "\n",
    "    lines = kdeplot(data=protein_variants_scaled, x='fitness', ax=ax).get_lines()\n",
    "        \n",
    "    for line in lines:\n",
    "        x, y = line.get_data()\n",
    "        mode=x[np.argmax(y)]\n",
    "    \n",
    "    G = nx.from_dict_of_lists(dod)\n",
    "    G_val = nx.from_dict_of_lists(dod)\n",
    "    \n",
    "    pos={}\n",
    "    for index, row in protein_variants_scaled.iterrows():\n",
    "        pos[row.core] = (row.Nham_aa-lowest_nham, row.fitness)\n",
    "        \n",
    "    validated = validated_variants[protein]\n",
    "    pos_val={}\n",
    "    for index, row in protein_variants_scaled[protein_variants_scaled['core'].isin(validated)].iterrows():\n",
    "        pos_val[row.core] = (row.Nham_aa-lowest_nham, row.fitness)\n",
    "\n",
    "    fig, (ax0,axviable,ax1,ax2) = plt.subplots(4,1,gridspec_kw={'height_ratios':[2,2,1,12],'hspace': 0.15},figsize=(2.3,2.5))\n",
    "    plt.close()\n",
    "    fig, (ax0,axviable,ax1,ax2) = plt.subplots(4,1,gridspec_kw={'height_ratios':[2,2,1,12],'hspace': 0.15},figsize=(2.3,2.5),sharex=ax1)\n",
    "    \n",
    "    \n",
    "    fig=sns.violinplot(x=\"Nham_aa\", y=\"fitness\", data=protein_variants_scaled, inner=\"point\", ax=ax2, color=protein_colors[protein], linewidth=0.3)\n",
    "    plt.axhline(y=-0.5, linestyle='--', color='gray', linewidth=0.5)\n",
    "    \n",
    "    remove=[i for i in dod.keys() if i not in pos.keys()]\n",
    "    remove_val=[i for i in dod.keys() if i not in pos_val.keys()]\n",
    "    \n",
    "    G.remove_nodes_from(remove)\n",
    "    G_val.remove_nodes_from(remove_val)\n",
    "    \n",
    "    fitness_values=[]\n",
    "    for i in pos:\n",
    "        fitness_values.append(pos[i][1])\n",
    "    groups = []\n",
    "    for _, g in groupby(fitness_values, lambda x: (x-1)//0.05):\n",
    "        groups.append(list(g))      # Store group iterator as a list\n",
    "    \n",
    "    fitness_attr={}\n",
    "    for node in G.nodes:\n",
    "        iterator=0\n",
    "        for bin in groups:\n",
    "            if pos[node][1] in bin:\n",
    "                fitness_attr[node]=iterator\n",
    "                break\n",
    "            iterator+=1\n",
    "    \n",
    "    nx.set_node_attributes(G, fitness_attr, 'fitness')\n",
    "    groups = set(nx.get_node_attributes(G,'fitness').values())\n",
    "    mapping = dict(zip(sorted(groups),count()))\n",
    "    nodes = G.nodes()\n",
    "    colors = [mapping[G.nodes[n]['fitness']] for n in nodes]\n",
    "    \n",
    "    total_viable=0\n",
    "    nham_viability=pd.DataFrame(columns=['Number_of_variants','Fraction_50pc_folded'])\n",
    "    for nham in range(lowest_nham,8,1):\n",
    "        nham_viability.at[nham,'protein']=protein\n",
    "        nham_viability.at[nham,'nham']=nham\n",
    "        nham_viability.at[nham,'Number_of_variants']=len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham])\n",
    "        nham_viability.at[nham,'Fraction_50pc_folded']=round(100*(len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled['fitness']>=-0.5].index)/len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham])),1)\n",
    "        nham_viability.at[nham,'Number_of_50pc_folded_variants']=len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled['fitness']>=-0.5].index)\n",
    "        nham_viability.at[nham,'Fraction_75pc_folded']=round(100*(len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled['fitness']>=-0.25].index)/len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham])),1)\n",
    "        nham_viability.at[nham,'Number_of_75pc_folded_variants']=len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled['fitness']>=-0.25].index)\n",
    "        nham_viability.at[nham,'Fraction_90pc_folded']=round(100*(len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled['fitness']>=-0.1].index)/len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham])),1)\n",
    "        nham_viability.at[nham,'Number_of_90pc_folded_variants']=len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled['fitness']>=-0.1].index)\n",
    "        total_viable=total_viable+len(protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==nham].loc[protein_variants_scaled['fitness']>=-0.5].index)\n",
    "    for index, row in nham_viability.iterrows():\n",
    "        nham_viability.at[index,'Fraction_of_total_50pc_viable_variants']= round(100*row['Number_of_50pc_folded_variants']/total_viable,1)\n",
    "    \n",
    "    total_core_viabilities.at[protein,'variants_above_01']=len(protein_variants_scaled.loc[protein_variants_scaled['fitness']>=-0.1].index)/len(protein_variants_scaled.index)*5**7\n",
    "    total_core_viabilities.at[protein,'variants_above_025']=len(protein_variants_scaled.loc[protein_variants_scaled['fitness']>=-0.25].index)/len(protein_variants_scaled.index)*5**7\n",
    "    total_core_viabilities.at[protein,'variants_below_05']=5**7-len(protein_variants_scaled.loc[protein_variants_scaled['fitness']>=-0.5].index)/len(protein_variants_scaled.index)*5**7\n",
    "    total_core_viabilities.at[protein,'variants_above_05']=len(protein_variants_scaled.loc[protein_variants_scaled['fitness']>=-0.5].index)/len(protein_variants_scaled.index)*5**7\n",
    "        \n",
    "    orig_cmap = plt.cm.coolwarm\n",
    "    iterator_cmap+=1\n",
    "    midpoint=(-0.5 - np.min(protein_variants_scaled['fitness']))/ (np.max(protein_variants_scaled['fitness']) - np.min(protein_variants_scaled['fitness']))\n",
    "    shifted_cmap = shiftedColorMap(orig_cmap, midpoint=1-midpoint, name='shifted'+str(iterator_cmap))\n",
    "        \n",
    "    nx.draw_networkx(G, pos=pos, with_labels=False, node_size=0, ax=ax2, edge_color='gray', width=0.01)\n",
    "    nodes=nx.draw_networkx_nodes(G, pos, nodelist=nodes, node_color=colors, node_size=0.3, cmap=shifted_cmap)\n",
    "    nx.draw_networkx(G_val, pos=pos_val, with_labels=False, node_size=10, ax=ax2, width=0, node_shape=matplotlib.markers.MarkerStyle(marker='o', fillstyle='none'), node_color='k')\n",
    "    ax2.tick_params(left=True, bottom=True, labelleft=True, labelbottom=True)\n",
    "    ax2.set_xlabel('Number of amino acid substitutions', fontsize=7)\n",
    "    ax2.set_ylabel(\"Abundance\\nfitness\", fontsize=7,rotation='horizontal', ha='right')\n",
    "    ax2.spines['top'].set_visible(False)\n",
    "    ax2.spines['right'].set_visible(False)\n",
    "    ax2.tick_params(axis='both', which='major', labelsize=6)\n",
    "    \n",
    "    nham_viability['Fraction_50pc_folded'] = pd.to_numeric(nham_viability['Fraction_50pc_folded'], errors='coerce')\n",
    "    sns.scatterplot(x=[i-lowest_nham for i in nham_viability['nham']],y=[0]*len(nham_viability.index), hue=nham_viability['Fraction_50pc_folded'],palette='coolwarm_r', s=nham_viability['Fraction_50pc_folded']*0.5,ax=ax1)\n",
    "    ax1.legend_.remove()\n",
    "    ax1.spines[['top','right','left','bottom']].set_visible(False)\n",
    "    ax1.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False,top=False, right=False)\n",
    "    ax1.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
    "    for index, row in nham_viability.iterrows():\n",
    "        if row.Fraction_50pc_folded == 100.0:\n",
    "            ax2.annotate(100, (row.nham-lowest_nham-0.4,0.4), fontsize=6, color='black')\n",
    "        else:\n",
    "            ax2.annotate(row.Fraction_50pc_folded, (row.nham-lowest_nham-0.3,0.4), fontsize=6, color='black')\n",
    "    ax1.set_ylabel('% variants\\nfitness > -0.5',fontsize=6,rotation='horizontal', ha='right')\n",
    "            \n",
    "    ax2.annotate('n='+f\"{len(protein_variants_scaled):,}\", (-0.3,-1.5), fontsize=6)\n",
    "    plt.subplots_adjust(hspace=0.05)\n",
    "    \n",
    "    nham_viability['Number_of_variants'].plot.bar(ax=ax0,fontsize=6,color='gray')\n",
    "    ax0.spines[['top','right']].set_visible(False)\n",
    "    ax0.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
    "    ax0.set_ylabel('Total count',fontsize=6, rotation='horizontal', ha='right')\n",
    "    labels = ax0.get_yticklabels()\n",
    "    labels[0] = \"\"\n",
    "    ax0.set_yticklabels(labels)\n",
    "    \n",
    "    nham_viability['Number_of_50pc_folded_variants'].plot.bar(ax=axviable,fontsize=6,color='gray')\n",
    "    axviable.spines[['top','right']].set_visible(False)\n",
    "    axviable.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
    "    axviable.set_ylabel('Nr. variants\\nfitness > -0.5',fontsize=6,rotation='horizontal', ha='right')\n",
    "    \n",
    "    ax0.get_yaxis().set_label_coords(-0.2,0)\n",
    "    axviable.get_yaxis().set_label_coords(-0.2,0)\n",
    "    ax1.get_yaxis().set_label_coords(-0.2,-1)\n",
    "    ax2.get_yaxis().set_label_coords(-0.2,0.5)\n",
    "    plt.ylim(-1.6,0.6)\n",
    "    plt.xlim(-0.5,7-lowest_nham+0.5)\n",
    "    plt.rcParams['svg.fonttype'] = 'none'\n",
    "    #plt.savefig(protein+'_fitness_distri.png', dpi=300, transparent=True, bbox_inches='tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02c3e565-8a38-45f4-bd7a-60cebfbf40e2",
   "metadata": {},
   "source": [
    "## Panel f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a9ac97e5-b4ae-428a-a4dd-e1ec473ba2a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from scipy.interpolate import UnivariateSpline, InterpolatedUnivariateSpline\n",
    "import math\n",
    "\n",
    "data=pd.read_csv(path+\"Fig1/Sparse_cores_DTS_Tecan.txt\", header=0, delim_whitespace=True)\n",
    "final_cycle_col=len(data.columns)-1\n",
    "data2=data\n",
    "data2=data2.drop(data2.index[[0,1]])\n",
    "data2.index=data2.Cycle\n",
    "data2 = data2.drop('Cycle', 1)\n",
    "plasmid_info=pd.read_csv(path+\"Fig1/Sparse_cores_DTS_Tecan_randomization.txt\", header=0, delim_whitespace=True)\n",
    "plasmid_info.index=plasmid_info.well\n",
    "plasmid_info = plasmid_info.drop('well',1)\n",
    "data2= pd.concat([data2, plasmid_info], axis=1)\n",
    "\n",
    "blanks = data2.loc[data2['plasmid'] == 'blank']\n",
    "blanks = pd.DataFrame(blanks.mean().to_dict(),index=[blanks.index.values[-1]])\n",
    "blanks = blanks.reset_index()\n",
    "del blanks['index']\n",
    "\n",
    "data2 = data2.drop('plasmid', 1)\n",
    "\n",
    "data3 = pd.DataFrame(columns=np.arange(1,final_cycle_col+1,1))\n",
    "for index, row in data2.iterrows():\n",
    "    a=list(row-blanks.loc[0])\n",
    "    data3.loc[index]=a\n",
    "\n",
    "\n",
    "data3= pd.concat([data3, plasmid_info], axis=1)\n",
    "#data3 = data3.drop('rand', 1)    \n",
    "\n",
    "data.index=data.Cycle\n",
    "data = data.drop('Cycle', 1)\n",
    "\n",
    "cols=np.arange(1,final_cycle_col+1,1)\n",
    "time=[]\n",
    "for i,j in zip(data.iloc[0],cols):\n",
    "    time.append(int(j)*921.3/3600)\n",
    "\n",
    "to_plot = list(plasmid_info['plasmid'].unique())[:-4]\n",
    "rows = pd.DataFrame(columns=np.arange(1,final_cycle_col+1,1))\n",
    "for i in to_plot:\n",
    "    rows = pd.concat([rows, data3.loc[data3['plasmid'] == i]], axis=0)\n",
    "    \n",
    "plt.figure(figsize=(4,8))\n",
    "plasmids=[]\n",
    "colors=[]\n",
    "for index,row in rows.iterrows():\n",
    "    if row['plasmid'] not in plasmids:\n",
    "        plasmids.append(row['plasmid'])\n",
    "        r = lambda: random.randint(0,255)\n",
    "        colors.append('#%02X%02X%02X' % (r(),r(),r()))\n",
    "    row = row.drop(['plasmid'])\n",
    "    row.index=time\n",
    "    #if max(row) > 0.8:\n",
    "    #    pass\n",
    "    #else:\n",
    "    x = time\n",
    "    y = row[:]\n",
    "    y_spl = UnivariateSpline(x,y, s=0.02)\n",
    "    l = final_cycle_col\n",
    "    x_range = np.linspace(x[0],x[l-1],1000)\n",
    "    plt.subplot(211)\n",
    "    plt.plot(x,y, 'o', markersize=3, c=colors[-1], label=plasmids[-1])\n",
    "    plt.plot(x_range, y_spl(x_range), '-', c=colors[-1], label=plasmids[-1])\n",
    "    plt.xlabel('time / h')\n",
    "    plt.ylabel('OD600')\n",
    "    #plt.ylim(0,0.5)\n",
    "    #Derivative  \n",
    "    deriv = y_spl.derivative()(x_range)\n",
    "    #2nd Derivative  \n",
    "    secnd_deriv = y_spl.derivative(n=2)(x_range)\n",
    "    #Plot derivative\n",
    "    plt.subplot(212)\n",
    "    plt.plot(x_range, deriv, c=colors[-1], label=plasmids[-1])\n",
    "    plt.xlabel('time / h')\n",
    "    plt.ylabel('dOD600')\n",
    "    dmax = max(deriv)\n",
    "    if dmax > 0.005:\n",
    "        cp = x_range[np.argmax(deriv)]\n",
    "    else:\n",
    "        cp = np.nan\n",
    "    data3.at[index,'growth_t']=cp\n",
    "    try:\n",
    "        data3.at[index,'growthrate']=math.log(y_spl(x_range[np.argmax(deriv)])/y_spl(x_range)[0])/cp\n",
    "    except ValueError:\n",
    "        data3.at[index,'growthrate']=math.log(y_spl(x_range[np.argmax(deriv)])/(-1*y_spl(x_range)[0]))/cp\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = dict(zip(labels, handles))\n",
    "plt.legend(by_label.values(), by_label.keys(),bbox_to_anchor = (2, 3.2),ncol=10)\n",
    "plt.show()\n",
    "\n",
    "for index, row in data3.iterrows():\n",
    "    slope = 0\n",
    "    if max(row[:-3]) > 0.1:\n",
    "        b=max(row[:-3])\n",
    "        a = min(i for i in row[:row[row == b].index[0]] if i > 0.05)\n",
    "        odt1=row[row == a].index[0]\n",
    "        if max(row[:row[row == b].index[0]]) < 0.3:\n",
    "            a = max(row[:row[row == b].index[0]])\n",
    "            odt2=row[row == a].index[0]-1\n",
    "        else:\n",
    "            a = min(i for i in row[:row[row == b].index[0]] if i > 0.3)\n",
    "            odt2=row[row == a].index[0]-1\n",
    "        odt1=1\n",
    "        odt2=time.index(30.71)\n",
    "        delta_t=(data.loc['Time'][odt2]-data.loc['Time'][odt1-1])/3600\n",
    "        try:\n",
    "            slope=math.log(row[odt2]/0.05)/delta_t\n",
    "        except ValueError:\n",
    "            continue\n",
    "    data3.at[index,'slope']=slope\n",
    "\n",
    "rows2 = pd.DataFrame()\n",
    "growth_t2 = pd.DataFrame()\n",
    "for i in to_plot:\n",
    "    rows = pd.DataFrame()\n",
    "    rows[i] = data3.loc[data3['plasmid'] == i]['slope']\n",
    "    rows=rows.reset_index()\n",
    "    rows2[i]=rows[i]\n",
    "    \n",
    "    growth_t = pd.DataFrame()\n",
    "    growth_t[i] = data3.loc[data3['plasmid'] == i]['growth_t']\n",
    "    growth_t=growth_t.reset_index()\n",
    "    growth_t2[i]=growth_t[i]\n",
    "\n",
    "rows2.plot.box(figsize=(16,6))\n",
    "plt.ylabel('growth rate')\n",
    "plt.xticks(rotation=90)\n",
    "growth_t2.plot.box(figsize=(16,6))\n",
    "plt.ylabel('growth time')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e0c44f8-6f48-44b4-8d94-7d4c4608ec08",
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_tecan=pd.DataFrame()\n",
    "for i in to_plot:\n",
    "    validation_tecan2=pd.DataFrame()\n",
    "    validation_tecan2[i]=data3.loc[data3['plasmid'] == i]['growthrate']\n",
    "    validation_tecan2=validation_tecan2.reset_index()\n",
    "    validation_tecan=pd.concat([validation_tecan, validation_tecan2[i]], axis=1)\n",
    "\n",
    "validation_tecan.columns=to_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "29ca3c6a-3c61-4803-96a3-36025998e9d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ids = pd.read_csv(path+\"Fig1/Sparse_cores_DTS_Tecan_ids.txt\", header=None, names=['protein', 'well', 'core'], sep='\\t', lineterminator='\\n')\n",
    "\n",
    "controls = ['pGJJ025', 'pGJJ034']\n",
    "\n",
    "def normalize_errors(values, bounds):\n",
    "    return [x * (bounds['desired']['upper'] - bounds['desired']['lower']) / (bounds['actual']['upper'] - bounds['actual']['lower']) for x in values]\n",
    "\n",
    "# Normalize data in the -1 to 0 range\n",
    "reference=allvariants.loc[allvariants['Protein']=='P06241'].sort_values(['Nham_aa','fitness'], ascending=[True,False]).iloc[0]['core']\n",
    "\n",
    "fig = plt.figure(figsize=(0,0))\n",
    "lines = kdeplot(data=allvariants,x='fitness',linewidth=0).get_lines()\n",
    "for line in lines:\n",
    "    x, y = line.get_data()\n",
    "    mode=(x[np.argmax(y)])\n",
    "    ax.axvline(x[np.argmax(y)], ls='--')\n",
    "plt.close()\n",
    "    \n",
    "allvariants_scaled = allvariants.copy()    \n",
    "\n",
    "allvariants_scaled['fitness']=normalize(list(allvariants_scaled['fitness']),{'actual': {'lower': mode, 'upper': allvariants_scaled.loc[allvariants_scaled['core']==reference]['fitness'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "allvariants_scaled['sigma']=normalize(list(allvariants_scaled['sigma']),{'actual': {'lower': mode, 'upper': allvariants_scaled.loc[allvariants_scaled['core']==reference]['fitness'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "\n",
    "compy_data = ids.merge(allvariants_scaled[['Protein', 'core', 'fitness', 'growthrate', 'growthrate_sigma','sigma', 'Nham_aa']],\n",
    "                                       left_on=['protein', 'core'],\n",
    "                                       right_on=['Protein', 'core'],\n",
    "                                       how='left').drop('Protein', axis=1)\n",
    "compy_data = compy_data.dropna(subset='fitness')\n",
    "compy_data = compy_data.sort_values('fitness', ascending = False)\n",
    "plasmids = list(set(list(plasmid_info['plasmid'].unique())[:-4]) - set(controls))\n",
    "compy_data_sub = compy_data[compy_data['well'].isin(plasmids)].sort_values('fitness', ascending = False)\n",
    "\n",
    "DMS_tecan = pd.DataFrame(validation_tecan.mean(skipna=True))\n",
    "DMS_tecan = DMS_tecan.rename(columns={0: 'Tecan_Growth_T'})\n",
    "DMS_tecan['Tecan_Growth_T_std']=validation_tecan.std(skipna=True)\n",
    "DMS_tecan = DMS_tecan.drop(['pGJJ025', 'pGJJ034'])\n",
    "DMS_tecan = DMS_tecan.reset_index()\n",
    "proteins = {'P06241':'FYN-SH3',\n",
    "            'P01053':'CI-2A',\n",
    "            'P0A9X9':'CspA'}\n",
    "\n",
    "DMS_tecan = DMS_tecan.merge(compy_data_sub, left_on=['index'], right_on=['well'], how='left').drop('index', axis=1)\n",
    "for index, row in DMS_tecan.iterrows():\n",
    "    try:\n",
    "        DMS_tecan.at[index,'prot_name']=proteins[row.protein]\n",
    "    except KeyError:\n",
    "        continue\n",
    "\n",
    "DMS_tecan = DMS_tecan.dropna()\n",
    "DMS_tecan.sort_values(by=\"prot_name\", key=lambda column: column.map(lambda e: list(proteins.values()).index(e)), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "efc97791-81de-4585-b83a-a23356121b93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tecan_Growth_T</th>\n",
       "      <th>Tecan_Growth_T_std</th>\n",
       "      <th>protein</th>\n",
       "      <th>well</th>\n",
       "      <th>core</th>\n",
       "      <th>fitness</th>\n",
       "      <th>growthrate</th>\n",
       "      <th>growthrate_sigma</th>\n",
       "      <th>sigma</th>\n",
       "      <th>Nham_aa</th>\n",
       "      <th>prot_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.068137</td>\n",
       "      <td>0.016418</td>\n",
       "      <td>P06241</td>\n",
       "      <td>F5</td>\n",
       "      <td>FLMIIVM</td>\n",
       "      <td>-0.273909</td>\n",
       "      <td>0.139818</td>\n",
       "      <td>0.008667</td>\n",
       "      <td>0.037897</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FYN-SH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.132123</td>\n",
       "      <td>0.026164</td>\n",
       "      <td>P06241</td>\n",
       "      <td>G8</td>\n",
       "      <td>MFFVMVF</td>\n",
       "      <td>-0.021236</td>\n",
       "      <td>0.189751</td>\n",
       "      <td>0.008139</td>\n",
       "      <td>0.035590</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FYN-SH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.106824</td>\n",
       "      <td>0.019620</td>\n",
       "      <td>P06241</td>\n",
       "      <td>E12</td>\n",
       "      <td>VVFFLIF</td>\n",
       "      <td>-0.246903</td>\n",
       "      <td>0.145155</td>\n",
       "      <td>0.008091</td>\n",
       "      <td>0.035378</td>\n",
       "      <td>4.0</td>\n",
       "      <td>FYN-SH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.076930</td>\n",
       "      <td>0.014839</td>\n",
       "      <td>P06241</td>\n",
       "      <td>E8</td>\n",
       "      <td>MIIVLVV</td>\n",
       "      <td>-0.408574</td>\n",
       "      <td>0.113205</td>\n",
       "      <td>0.009405</td>\n",
       "      <td>0.041127</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FYN-SH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.079112</td>\n",
       "      <td>0.020756</td>\n",
       "      <td>P06241</td>\n",
       "      <td>C9</td>\n",
       "      <td>MVVMVIM</td>\n",
       "      <td>-0.854345</td>\n",
       "      <td>0.025112</td>\n",
       "      <td>0.010290</td>\n",
       "      <td>0.044996</td>\n",
       "      <td>6.0</td>\n",
       "      <td>FYN-SH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.085144</td>\n",
       "      <td>0.032347</td>\n",
       "      <td>P06241</td>\n",
       "      <td>F6</td>\n",
       "      <td>MVLMVVM</td>\n",
       "      <td>-0.745152</td>\n",
       "      <td>0.046691</td>\n",
       "      <td>0.007253</td>\n",
       "      <td>0.031717</td>\n",
       "      <td>7.0</td>\n",
       "      <td>FYN-SH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.047624</td>\n",
       "      <td>0.010335</td>\n",
       "      <td>P01053</td>\n",
       "      <td>D5</td>\n",
       "      <td>IFFVVLL</td>\n",
       "      <td>-1.074334</td>\n",
       "      <td>-0.018362</td>\n",
       "      <td>0.012907</td>\n",
       "      <td>0.056438</td>\n",
       "      <td>6.0</td>\n",
       "      <td>CI-2A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.112042</td>\n",
       "      <td>0.057397</td>\n",
       "      <td>P01053</td>\n",
       "      <td>D2</td>\n",
       "      <td>VVVILVV</td>\n",
       "      <td>-0.172161</td>\n",
       "      <td>0.159925</td>\n",
       "      <td>0.006593</td>\n",
       "      <td>0.028829</td>\n",
       "      <td>4.0</td>\n",
       "      <td>CI-2A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.150731</td>\n",
       "      <td>0.013364</td>\n",
       "      <td>P01053</td>\n",
       "      <td>E2</td>\n",
       "      <td>IIVIVVV</td>\n",
       "      <td>0.049755</td>\n",
       "      <td>0.203780</td>\n",
       "      <td>0.008306</td>\n",
       "      <td>0.036321</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CI-2A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.174838</td>\n",
       "      <td>0.057235</td>\n",
       "      <td>P01053</td>\n",
       "      <td>A9</td>\n",
       "      <td>VIFIIVV</td>\n",
       "      <td>0.158590</td>\n",
       "      <td>0.225288</td>\n",
       "      <td>0.006706</td>\n",
       "      <td>0.029325</td>\n",
       "      <td>4.0</td>\n",
       "      <td>CI-2A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.058563</td>\n",
       "      <td>0.013654</td>\n",
       "      <td>P01053</td>\n",
       "      <td>B8</td>\n",
       "      <td>VVVILFI</td>\n",
       "      <td>-0.765035</td>\n",
       "      <td>0.042762</td>\n",
       "      <td>0.010868</td>\n",
       "      <td>0.047525</td>\n",
       "      <td>4.0</td>\n",
       "      <td>CI-2A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.045423</td>\n",
       "      <td>0.016918</td>\n",
       "      <td>P0A9X9</td>\n",
       "      <td>D12</td>\n",
       "      <td>VVMVFMM</td>\n",
       "      <td>-0.752074</td>\n",
       "      <td>0.045323</td>\n",
       "      <td>0.008730</td>\n",
       "      <td>0.038174</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CspA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.100180</td>\n",
       "      <td>0.025828</td>\n",
       "      <td>P0A9X9</td>\n",
       "      <td>E7</td>\n",
       "      <td>VILVVFV</td>\n",
       "      <td>-0.119153</td>\n",
       "      <td>0.170400</td>\n",
       "      <td>0.008315</td>\n",
       "      <td>0.036358</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CspA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.082835</td>\n",
       "      <td>0.048996</td>\n",
       "      <td>P0A9X9</td>\n",
       "      <td>H6</td>\n",
       "      <td>VVMMLVV</td>\n",
       "      <td>-0.556839</td>\n",
       "      <td>0.083905</td>\n",
       "      <td>0.009425</td>\n",
       "      <td>0.041214</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CspA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Tecan_Growth_T  Tecan_Growth_T_std protein well     core   fitness  \\\n",
       "2         0.068137            0.016418  P06241   F5  FLMIIVM -0.273909   \n",
       "3         0.132123            0.026164  P06241   G8  MFFVMVF -0.021236   \n",
       "6         0.106824            0.019620  P06241  E12  VVFFLIF -0.246903   \n",
       "8         0.076930            0.014839  P06241   E8  MIIVLVV -0.408574   \n",
       "15        0.079112            0.020756  P06241   C9  MVVMVIM -0.854345   \n",
       "17        0.085144            0.032347  P06241   F6  MVLMVVM -0.745152   \n",
       "5         0.047624            0.010335  P01053   D5  IFFVVLL -1.074334   \n",
       "9         0.112042            0.057397  P01053   D2  VVVILVV -0.172161   \n",
       "13        0.150731            0.013364  P01053   E2  IIVIVVV  0.049755   \n",
       "16        0.174838            0.057235  P01053   A9  VIFIIVV  0.158590   \n",
       "19        0.058563            0.013654  P01053   B8  VVVILFI -0.765035   \n",
       "0         0.045423            0.016918  P0A9X9  D12  VVMVFMM -0.752074   \n",
       "1         0.100180            0.025828  P0A9X9   E7  VILVVFV -0.119153   \n",
       "12        0.082835            0.048996  P0A9X9   H6  VVMMLVV -0.556839   \n",
       "\n",
       "    growthrate  growthrate_sigma     sigma  Nham_aa prot_name  \n",
       "2     0.139818          0.008667  0.037897      4.0   FYN-SH3  \n",
       "3     0.189751          0.008139  0.035590      6.0   FYN-SH3  \n",
       "6     0.145155          0.008091  0.035378      4.0   FYN-SH3  \n",
       "8     0.113205          0.009405  0.041127      6.0   FYN-SH3  \n",
       "15    0.025112          0.010290  0.044996      6.0   FYN-SH3  \n",
       "17    0.046691          0.007253  0.031717      7.0   FYN-SH3  \n",
       "5    -0.018362          0.012907  0.056438      6.0     CI-2A  \n",
       "9     0.159925          0.006593  0.028829      4.0     CI-2A  \n",
       "13    0.203780          0.008306  0.036321      5.0     CI-2A  \n",
       "16    0.225288          0.006706  0.029325      4.0     CI-2A  \n",
       "19    0.042762          0.010868  0.047525      4.0     CI-2A  \n",
       "0     0.045323          0.008730  0.038174      5.0      CspA  \n",
       "1     0.170400          0.008315  0.036358      1.0      CspA  \n",
       "12    0.083905          0.009425  0.041214      5.0      CspA  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DMS_tecan = pd.DataFrame(validation_tecan.mean(skipna=True))\n",
    "DMS_tecan = DMS_tecan.rename(columns={0: 'Tecan_Growth_T'})\n",
    "DMS_tecan['Tecan_Growth_T_std']=validation_tecan.std(skipna=True)\n",
    "DMS_tecan = DMS_tecan.drop(['pGJJ025', 'pGJJ034'])\n",
    "DMS_tecan = DMS_tecan.reset_index()\n",
    "proteins = {'P06241':'FYN-SH3',\n",
    "            'P01053':'CI-2A',\n",
    "            'P0A9X9':'CspA'}\n",
    "\n",
    "DMS_tecan = DMS_tecan.merge(compy_data_sub, left_on=['index'], right_on=['well'], how='left').drop('index', axis=1)\n",
    "for index, row in DMS_tecan.iterrows():\n",
    "    try:\n",
    "        DMS_tecan.at[index,'prot_name']=proteins[row.protein]\n",
    "    except KeyError:\n",
    "        continue\n",
    "\n",
    "DMS_tecan = DMS_tecan.dropna()\n",
    "DMS_tecan.sort_values(by=\"prot_name\", key=lambda column: column.map(lambda e: list(proteins.values()).index(e)), inplace=True)\n",
    "DMS_tecan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1c2dfd51-a127-477c-93e6-f9a2c9f6dead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 120x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"Arial\"\n",
    "\n",
    "custom_colors = ['#F6DCC9', '#B4D689', '#B1D9D8']\n",
    "\n",
    "DMS_tecan=DMS_tecan.sort_values('fitness')\n",
    "\n",
    "\n",
    "g = sns.FacetGrid(data=DMS_tecan, hue='prot_name', aspect=2,palette=custom_colors)\n",
    "g.map(plt.errorbar, 'fitness', 'Tecan_Growth_T', 'Tecan_Growth_T_std','sigma', fmt='o', elinewidth=0.5, capsize=2, capthick=0.5, markersize=2)\n",
    "g.add_legend(bbox_to_anchor=(1.5,0.65), title='', fontsize=6)\n",
    "g.fig.set_size_inches(1.2,1.2)\n",
    "g.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "x = DMS_tecan[['fitness']].to_numpy()\n",
    "y = DMS_tecan[['Tecan_Growth_T']].to_numpy()\n",
    "\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x, y)\n",
    "\n",
    "y_pred = model.predict(x)\n",
    "r2 = r2_score(y,y_pred)\n",
    "r,p = pearsonr(y.ravel(),y_pred.ravel())\n",
    "\n",
    "plt.plot(x, y_pred, color='Gray', linewidth=1, linestyle='--')\n",
    "plt.xlabel('Abundance fitness', fontsize=7)\n",
    "plt.ylabel('Individual measurement\\nGrowth rate', fontsize=7)\n",
    "plt.annotate('R = '+str(round(r,2))+'\\np-val = '+'{:0.2e}'.format(p),xy=(-1.2,0.28),fontsize=7, annotation_clip=False)\n",
    "plt.annotate('n = '+str(len(x)),xy=(-1.1,0.22),fontsize=7, annotation_clip=False)\n",
    "plt.xticks(np.arange(-1,0.5,0.5),np.arange(-1,0.5,0.5))\n",
    "plt.yticks(np.arange(0,0.3,0.05),[f\"{round(x,2):.2f}\" for x in np.arange(0,0.3,0.05)])\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('Sparse_cores_DTS_Tecan.svg', dpi=300, transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d50915d-f59a-45ff-a340-bf4368f4790a",
   "metadata": {},
   "source": [
    "## Panel g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8a5f084a-10f1-42bb-8976-34bc07009273",
   "metadata": {},
   "outputs": [],
   "source": [
    "rocklin23 = pd.read_csv(path+\"Fig1/Tsuboyama2023_Dataset2_Dataset3_20230416.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "77cef64b-ba21-4143-addb-7be5ca67d140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>dna_seq</th>\n",
       "      <th>log10_K50_t</th>\n",
       "      <th>log10_K50_t_95CI_high</th>\n",
       "      <th>log10_K50_t_95CI_low</th>\n",
       "      <th>log10_K50_t_95CI</th>\n",
       "      <th>fitting_error_t</th>\n",
       "      <th>log10_K50unfolded_t</th>\n",
       "      <th>deltaG_t</th>\n",
       "      <th>deltaG_t_95CI_high</th>\n",
       "      <th>...</th>\n",
       "      <th>dG_ML</th>\n",
       "      <th>ddG_ML</th>\n",
       "      <th>Stabilizing_mut</th>\n",
       "      <th>pair_name</th>\n",
       "      <th>len_aa_seq</th>\n",
       "      <th>core</th>\n",
       "      <th>mutant_class</th>\n",
       "      <th>mutations</th>\n",
       "      <th>Nham</th>\n",
       "      <th>redundant_single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>478964</th>\n",
       "      <td>1MJC.pdb_V7M</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCATGAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>1.582470</td>\n",
       "      <td>1.641838</td>\n",
       "      <td>1.509769</td>\n",
       "      <td>0.132069</td>\n",
       "      <td>0.074082</td>\n",
       "      <td>-1.056253</td>\n",
       "      <td>3.597423</td>\n",
       "      <td>3.688685</td>\n",
       "      <td>...</td>\n",
       "      <td>3.856210348390495</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>MIVVVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V4M]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478965</th>\n",
       "      <td>1MJC.pdb_V7L</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCCTGAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>1.703320</td>\n",
       "      <td>1.750215</td>\n",
       "      <td>1.646387</td>\n",
       "      <td>0.103828</td>\n",
       "      <td>0.095938</td>\n",
       "      <td>-1.014201</td>\n",
       "      <td>3.729057</td>\n",
       "      <td>3.804507</td>\n",
       "      <td>...</td>\n",
       "      <td>3.973727430663791</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>LIVVVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V4L]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478966</th>\n",
       "      <td>1MJC.pdb_V7I</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCATCAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.340502</td>\n",
       "      <td>2.447400</td>\n",
       "      <td>2.157805</td>\n",
       "      <td>0.289595</td>\n",
       "      <td>0.097654</td>\n",
       "      <td>-0.991808</td>\n",
       "      <td>5.134258</td>\n",
       "      <td>5.852004</td>\n",
       "      <td>...</td>\n",
       "      <td>&gt;5</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>IIVVVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V4I]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478969</th>\n",
       "      <td>1MJC.pdb_V7F</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCTTCAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>0.891026</td>\n",
       "      <td>0.951695</td>\n",
       "      <td>0.825583</td>\n",
       "      <td>0.126113</td>\n",
       "      <td>0.094009</td>\n",
       "      <td>-1.039906</td>\n",
       "      <td>2.586367</td>\n",
       "      <td>2.670486</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0026899237542777</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>FIVVVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V4F]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479228</th>\n",
       "      <td>1MJC.pdb_I19M</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATTGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>0.561132</td>\n",
       "      <td>0.596110</td>\n",
       "      <td>0.533784</td>\n",
       "      <td>0.062326</td>\n",
       "      <td>0.116045</td>\n",
       "      <td>-1.006768</td>\n",
       "      <td>2.084742</td>\n",
       "      <td>2.133267</td>\n",
       "      <td>...</td>\n",
       "      <td>2.511358859110776</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VMVVVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[I16M]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479229</th>\n",
       "      <td>1MJC.pdb_I19L</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.837781</td>\n",
       "      <td>2.922924</td>\n",
       "      <td>2.636747</td>\n",
       "      <td>0.286177</td>\n",
       "      <td>0.145708</td>\n",
       "      <td>-0.972818</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>&gt;5</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VLVVVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[I16L]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479230</th>\n",
       "      <td>1MJC.pdb_I19V</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.062723</td>\n",
       "      <td>2.103929</td>\n",
       "      <td>1.995817</td>\n",
       "      <td>0.108113</td>\n",
       "      <td>0.047473</td>\n",
       "      <td>-0.961045</td>\n",
       "      <td>4.301419</td>\n",
       "      <td>4.390721</td>\n",
       "      <td>...</td>\n",
       "      <td>4.453555303991211</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VVVVVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[I16V]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479233</th>\n",
       "      <td>1MJC.pdb_I19F</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>0.565862</td>\n",
       "      <td>0.582672</td>\n",
       "      <td>0.546140</td>\n",
       "      <td>0.036532</td>\n",
       "      <td>0.053713</td>\n",
       "      <td>-0.975123</td>\n",
       "      <td>2.047839</td>\n",
       "      <td>2.071211</td>\n",
       "      <td>...</td>\n",
       "      <td>2.2474702472980845</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VFVVVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[I16F]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479426</th>\n",
       "      <td>1MJC.pdb_V28M</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.101641</td>\n",
       "      <td>2.171323</td>\n",
       "      <td>2.028858</td>\n",
       "      <td>0.142465</td>\n",
       "      <td>0.120709</td>\n",
       "      <td>-1.003845</td>\n",
       "      <td>4.442824</td>\n",
       "      <td>4.607360</td>\n",
       "      <td>...</td>\n",
       "      <td>4.781802793554261</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIMVVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V25M]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479427</th>\n",
       "      <td>1MJC.pdb_V28L</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.411118</td>\n",
       "      <td>2.551303</td>\n",
       "      <td>2.360266</td>\n",
       "      <td>0.191036</td>\n",
       "      <td>0.082703</td>\n",
       "      <td>-0.993921</td>\n",
       "      <td>5.525658</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>&gt;5</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VILVVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V25L]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479428</th>\n",
       "      <td>1MJC.pdb_V28I</td>\n",
       "      <td>TCTGCTGGCAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.447673</td>\n",
       "      <td>2.531646</td>\n",
       "      <td>2.398884</td>\n",
       "      <td>0.132762</td>\n",
       "      <td>0.083460</td>\n",
       "      <td>-0.988318</td>\n",
       "      <td>5.850543</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>&gt;5</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIIVVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V25I]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479431</th>\n",
       "      <td>1MJC.pdb_V28F</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.069291</td>\n",
       "      <td>2.160740</td>\n",
       "      <td>1.973884</td>\n",
       "      <td>0.186855</td>\n",
       "      <td>0.163611</td>\n",
       "      <td>-1.012599</td>\n",
       "      <td>4.384218</td>\n",
       "      <td>4.592704</td>\n",
       "      <td>...</td>\n",
       "      <td>4.395952509682019</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIFVVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V25F]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479470</th>\n",
       "      <td>1MJC.pdb_V30M</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.248745</td>\n",
       "      <td>2.302489</td>\n",
       "      <td>2.198038</td>\n",
       "      <td>0.104451</td>\n",
       "      <td>0.083123</td>\n",
       "      <td>-1.036614</td>\n",
       "      <td>4.864412</td>\n",
       "      <td>5.043002</td>\n",
       "      <td>...</td>\n",
       "      <td>4.889611851282988</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVMVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V27M]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479471</th>\n",
       "      <td>1MJC.pdb_V30L</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.112130</td>\n",
       "      <td>2.283248</td>\n",
       "      <td>1.985986</td>\n",
       "      <td>0.297262</td>\n",
       "      <td>0.132326</td>\n",
       "      <td>-1.017041</td>\n",
       "      <td>4.483995</td>\n",
       "      <td>4.949053</td>\n",
       "      <td>...</td>\n",
       "      <td>&gt;5</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVLVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V27L]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479472</th>\n",
       "      <td>1MJC.pdb_V30I</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.270224</td>\n",
       "      <td>2.343645</td>\n",
       "      <td>2.230493</td>\n",
       "      <td>0.113153</td>\n",
       "      <td>0.057891</td>\n",
       "      <td>-0.996057</td>\n",
       "      <td>4.877726</td>\n",
       "      <td>5.153708</td>\n",
       "      <td>...</td>\n",
       "      <td>&gt;5</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVIVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V27I]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479475</th>\n",
       "      <td>1MJC.pdb_V30F</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.691073</td>\n",
       "      <td>2.897694</td>\n",
       "      <td>2.541091</td>\n",
       "      <td>0.356603</td>\n",
       "      <td>0.165026</td>\n",
       "      <td>-1.031301</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>&gt;5</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVFVFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V27F]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479888</th>\n",
       "      <td>1MJC.pdb_V49M</td>\n",
       "      <td>TCCGCTGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>1.009775</td>\n",
       "      <td>1.081510</td>\n",
       "      <td>0.960138</td>\n",
       "      <td>0.121372</td>\n",
       "      <td>0.132921</td>\n",
       "      <td>-1.041657</td>\n",
       "      <td>2.753580</td>\n",
       "      <td>2.853665</td>\n",
       "      <td>...</td>\n",
       "      <td>3.058886952136891</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVVMFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V46M]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479889</th>\n",
       "      <td>1MJC.pdb_V49L</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>1.620531</td>\n",
       "      <td>1.667306</td>\n",
       "      <td>1.590431</td>\n",
       "      <td>0.076875</td>\n",
       "      <td>0.054207</td>\n",
       "      <td>-1.022071</td>\n",
       "      <td>3.609969</td>\n",
       "      <td>3.682689</td>\n",
       "      <td>...</td>\n",
       "      <td>3.861358132790528</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVVLFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V46L]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479890</th>\n",
       "      <td>1MJC.pdb_V49I</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.396126</td>\n",
       "      <td>2.519559</td>\n",
       "      <td>2.307481</td>\n",
       "      <td>0.212077</td>\n",
       "      <td>0.115334</td>\n",
       "      <td>-0.997413</td>\n",
       "      <td>5.429532</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.765744486627144</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVVIFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V46I]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479893</th>\n",
       "      <td>1MJC.pdb_V49F</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>0.346146</td>\n",
       "      <td>0.357090</td>\n",
       "      <td>0.336018</td>\n",
       "      <td>0.021072</td>\n",
       "      <td>0.053956</td>\n",
       "      <td>-1.050857</td>\n",
       "      <td>1.846052</td>\n",
       "      <td>1.861374</td>\n",
       "      <td>...</td>\n",
       "      <td>2.039578002501454</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVVFFV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V46F]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479932</th>\n",
       "      <td>1MJC.pdb_F51M</td>\n",
       "      <td>TCCGCGGGTAAGATGACTGGTATCGTTAAATGGTTCAACGCTGACA...</td>\n",
       "      <td>1.721156</td>\n",
       "      <td>1.772706</td>\n",
       "      <td>1.676092</td>\n",
       "      <td>0.096614</td>\n",
       "      <td>0.108578</td>\n",
       "      <td>-0.951567</td>\n",
       "      <td>3.673748</td>\n",
       "      <td>3.757509</td>\n",
       "      <td>...</td>\n",
       "      <td>3.8202295574031098</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVVVMV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[F48M]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479933</th>\n",
       "      <td>1MJC.pdb_F51L</td>\n",
       "      <td>TCCGCTGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>1.694036</td>\n",
       "      <td>1.742322</td>\n",
       "      <td>1.626705</td>\n",
       "      <td>0.115617</td>\n",
       "      <td>0.081059</td>\n",
       "      <td>-0.952241</td>\n",
       "      <td>3.631386</td>\n",
       "      <td>3.708788</td>\n",
       "      <td>...</td>\n",
       "      <td>3.589357535571305</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVVVLV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[F48L]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479934</th>\n",
       "      <td>1MJC.pdb_F51V</td>\n",
       "      <td>TCCGCTGGTAAAATGACCGGTATTGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>0.217265</td>\n",
       "      <td>0.233868</td>\n",
       "      <td>0.208290</td>\n",
       "      <td>0.025578</td>\n",
       "      <td>0.037596</td>\n",
       "      <td>-0.926328</td>\n",
       "      <td>1.487057</td>\n",
       "      <td>1.511030</td>\n",
       "      <td>...</td>\n",
       "      <td>1.532384887179894</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVVVVV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[F48V]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479935</th>\n",
       "      <td>1MJC.pdb_F51I</td>\n",
       "      <td>TCTGCGGGCAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>0.710245</td>\n",
       "      <td>0.736410</td>\n",
       "      <td>0.680552</td>\n",
       "      <td>0.055858</td>\n",
       "      <td>0.088759</td>\n",
       "      <td>-0.937318</td>\n",
       "      <td>2.196601</td>\n",
       "      <td>2.232919</td>\n",
       "      <td>...</td>\n",
       "      <td>2.4122586381324247</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVVVIV</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[F48I]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480240</th>\n",
       "      <td>1MJC.pdb_V65M</td>\n",
       "      <td>TCTGCTGGTAAGATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>0.945490</td>\n",
       "      <td>0.973143</td>\n",
       "      <td>0.870179</td>\n",
       "      <td>0.102964</td>\n",
       "      <td>0.055708</td>\n",
       "      <td>-1.000296</td>\n",
       "      <td>2.608395</td>\n",
       "      <td>2.646831</td>\n",
       "      <td>...</td>\n",
       "      <td>2.666230706177455</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVVVFM</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V62M]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480241</th>\n",
       "      <td>1MJC.pdb_V65L</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.144575</td>\n",
       "      <td>2.177636</td>\n",
       "      <td>2.082481</td>\n",
       "      <td>0.095155</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>-0.994648</td>\n",
       "      <td>4.529484</td>\n",
       "      <td>4.611062</td>\n",
       "      <td>...</td>\n",
       "      <td>4.58737109918793</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVVVFL</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V62L]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480242</th>\n",
       "      <td>1MJC.pdb_V65I</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>2.161215</td>\n",
       "      <td>2.215008</td>\n",
       "      <td>2.109769</td>\n",
       "      <td>0.105239</td>\n",
       "      <td>0.074522</td>\n",
       "      <td>-0.998995</td>\n",
       "      <td>4.575696</td>\n",
       "      <td>4.716210</td>\n",
       "      <td>...</td>\n",
       "      <td>4.775422814132017</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVVVFI</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V62I]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480245</th>\n",
       "      <td>1MJC.pdb_V65F</td>\n",
       "      <td>TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...</td>\n",
       "      <td>0.198259</td>\n",
       "      <td>0.235300</td>\n",
       "      <td>0.177760</td>\n",
       "      <td>0.057540</td>\n",
       "      <td>0.053722</td>\n",
       "      <td>-1.022148</td>\n",
       "      <td>1.596745</td>\n",
       "      <td>1.649502</td>\n",
       "      <td>...</td>\n",
       "      <td>1.8125709946469333</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>VIVVVFF</td>\n",
       "      <td>core_DTS</td>\n",
       "      <td>[V62F]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name                                            dna_seq  \\\n",
       "478964   1MJC.pdb_V7M  TCTGCGGGTAAAATGACCGGTATCATGAAATGGTTCAACGCGGACA...   \n",
       "478965   1MJC.pdb_V7L  TCTGCGGGTAAAATGACCGGTATCCTGAAATGGTTCAACGCGGACA...   \n",
       "478966   1MJC.pdb_V7I  TCTGCGGGTAAAATGACCGGTATCATCAAATGGTTCAACGCGGACA...   \n",
       "478969   1MJC.pdb_V7F  TCTGCGGGTAAAATGACCGGTATCTTCAAATGGTTCAACGCGGACA...   \n",
       "479228  1MJC.pdb_I19M  TCTGCGGGTAAAATGACCGGTATTGTTAAATGGTTCAACGCGGACA...   \n",
       "479229  1MJC.pdb_I19L  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479230  1MJC.pdb_I19V  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479233  1MJC.pdb_I19F  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479426  1MJC.pdb_V28M  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479427  1MJC.pdb_V28L  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479428  1MJC.pdb_V28I  TCTGCTGGCAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479431  1MJC.pdb_V28F  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479470  1MJC.pdb_V30M  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479471  1MJC.pdb_V30L  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479472  1MJC.pdb_V30I  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479475  1MJC.pdb_V30F  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479888  1MJC.pdb_V49M  TCCGCTGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479889  1MJC.pdb_V49L  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479890  1MJC.pdb_V49I  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479893  1MJC.pdb_V49F  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479932  1MJC.pdb_F51M  TCCGCGGGTAAGATGACTGGTATCGTTAAATGGTTCAACGCTGACA...   \n",
       "479933  1MJC.pdb_F51L  TCCGCTGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "479934  1MJC.pdb_F51V  TCCGCTGGTAAAATGACCGGTATTGTTAAATGGTTCAACGCGGACA...   \n",
       "479935  1MJC.pdb_F51I  TCTGCGGGCAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "480240  1MJC.pdb_V65M  TCTGCTGGTAAGATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "480241  1MJC.pdb_V65L  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "480242  1MJC.pdb_V65I  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "480245  1MJC.pdb_V65F  TCTGCGGGTAAAATGACCGGTATCGTTAAATGGTTCAACGCGGACA...   \n",
       "\n",
       "        log10_K50_t  log10_K50_t_95CI_high  log10_K50_t_95CI_low  \\\n",
       "478964     1.582470               1.641838              1.509769   \n",
       "478965     1.703320               1.750215              1.646387   \n",
       "478966     2.340502               2.447400              2.157805   \n",
       "478969     0.891026               0.951695              0.825583   \n",
       "479228     0.561132               0.596110              0.533784   \n",
       "479229     2.837781               2.922924              2.636747   \n",
       "479230     2.062723               2.103929              1.995817   \n",
       "479233     0.565862               0.582672              0.546140   \n",
       "479426     2.101641               2.171323              2.028858   \n",
       "479427     2.411118               2.551303              2.360266   \n",
       "479428     2.447673               2.531646              2.398884   \n",
       "479431     2.069291               2.160740              1.973884   \n",
       "479470     2.248745               2.302489              2.198038   \n",
       "479471     2.112130               2.283248              1.985986   \n",
       "479472     2.270224               2.343645              2.230493   \n",
       "479475     2.691073               2.897694              2.541091   \n",
       "479888     1.009775               1.081510              0.960138   \n",
       "479889     1.620531               1.667306              1.590431   \n",
       "479890     2.396126               2.519559              2.307481   \n",
       "479893     0.346146               0.357090              0.336018   \n",
       "479932     1.721156               1.772706              1.676092   \n",
       "479933     1.694036               1.742322              1.626705   \n",
       "479934     0.217265               0.233868              0.208290   \n",
       "479935     0.710245               0.736410              0.680552   \n",
       "480240     0.945490               0.973143              0.870179   \n",
       "480241     2.144575               2.177636              2.082481   \n",
       "480242     2.161215               2.215008              2.109769   \n",
       "480245     0.198259               0.235300              0.177760   \n",
       "\n",
       "        log10_K50_t_95CI  fitting_error_t  log10_K50unfolded_t   deltaG_t  \\\n",
       "478964          0.132069         0.074082            -1.056253   3.597423   \n",
       "478965          0.103828         0.095938            -1.014201   3.729057   \n",
       "478966          0.289595         0.097654            -0.991808   5.134258   \n",
       "478969          0.126113         0.094009            -1.039906   2.586367   \n",
       "479228          0.062326         0.116045            -1.006768   2.084742   \n",
       "479229          0.286177         0.145708            -0.972818  15.000000   \n",
       "479230          0.108113         0.047473            -0.961045   4.301419   \n",
       "479233          0.036532         0.053713            -0.975123   2.047839   \n",
       "479426          0.142465         0.120709            -1.003845   4.442824   \n",
       "479427          0.191036         0.082703            -0.993921   5.525658   \n",
       "479428          0.132762         0.083460            -0.988318   5.850543   \n",
       "479431          0.186855         0.163611            -1.012599   4.384218   \n",
       "479470          0.104451         0.083123            -1.036614   4.864412   \n",
       "479471          0.297262         0.132326            -1.017041   4.483995   \n",
       "479472          0.113153         0.057891            -0.996057   4.877726   \n",
       "479475          0.356603         0.165026            -1.031301  15.000000   \n",
       "479888          0.121372         0.132921            -1.041657   2.753580   \n",
       "479889          0.076875         0.054207            -1.022071   3.609969   \n",
       "479890          0.212077         0.115334            -0.997413   5.429532   \n",
       "479893          0.021072         0.053956            -1.050857   1.846052   \n",
       "479932          0.096614         0.108578            -0.951567   3.673748   \n",
       "479933          0.115617         0.081059            -0.952241   3.631386   \n",
       "479934          0.025578         0.037596            -0.926328   1.487057   \n",
       "479935          0.055858         0.088759            -0.937318   2.196601   \n",
       "480240          0.102964         0.055708            -1.000296   2.608395   \n",
       "480241          0.095155         0.066084            -0.994648   4.529484   \n",
       "480242          0.105239         0.074522            -0.998995   4.575696   \n",
       "480245          0.057540         0.053722            -1.022148   1.596745   \n",
       "\n",
       "        deltaG_t_95CI_high  ...               dG_ML  ddG_ML  Stabilizing_mut  \\\n",
       "478964            3.688685  ...   3.856210348390495       -                -   \n",
       "478965            3.804507  ...   3.973727430663791       -                -   \n",
       "478966            5.852004  ...                  >5       -                -   \n",
       "478969            2.670486  ...  3.0026899237542777       -                -   \n",
       "479228            2.133267  ...   2.511358859110776       -                -   \n",
       "479229           25.000000  ...                  >5       -                -   \n",
       "479230            4.390721  ...   4.453555303991211       -                -   \n",
       "479233            2.071211  ...  2.2474702472980845       -                -   \n",
       "479426            4.607360  ...   4.781802793554261       -                -   \n",
       "479427           25.000000  ...                  >5       -                -   \n",
       "479428           25.000000  ...                  >5       -                -   \n",
       "479431            4.592704  ...   4.395952509682019       -                -   \n",
       "479470            5.043002  ...   4.889611851282988       -                -   \n",
       "479471            4.949053  ...                  >5       -                -   \n",
       "479472            5.153708  ...                  >5       -                -   \n",
       "479475           25.000000  ...                  >5       -                -   \n",
       "479888            2.853665  ...   3.058886952136891       -                -   \n",
       "479889            3.682689  ...   3.861358132790528       -                -   \n",
       "479890           25.000000  ...   4.765744486627144       -                -   \n",
       "479893            1.861374  ...   2.039578002501454       -                -   \n",
       "479932            3.757509  ...  3.8202295574031098       -                -   \n",
       "479933            3.708788  ...   3.589357535571305       -                -   \n",
       "479934            1.511030  ...   1.532384887179894       -                -   \n",
       "479935            2.232919  ...  2.4122586381324247       -                -   \n",
       "480240            2.646831  ...   2.666230706177455       -                -   \n",
       "480241            4.611062  ...    4.58737109918793       -                -   \n",
       "480242            4.716210  ...   4.775422814132017       -                -   \n",
       "480245            1.649502  ...  1.8125709946469333       -                -   \n",
       "\n",
       "        pair_name  len_aa_seq     core  mutant_class  mutations  Nham  \\\n",
       "478964        NaN        68.0  MIVVVFV      core_DTS      [V4M]   1.0   \n",
       "478965        NaN        68.0  LIVVVFV      core_DTS      [V4L]   1.0   \n",
       "478966        NaN        68.0  IIVVVFV      core_DTS      [V4I]   1.0   \n",
       "478969        NaN        68.0  FIVVVFV      core_DTS      [V4F]   1.0   \n",
       "479228        NaN        68.0  VMVVVFV      core_DTS     [I16M]   1.0   \n",
       "479229        NaN        68.0  VLVVVFV      core_DTS     [I16L]   1.0   \n",
       "479230        NaN        68.0  VVVVVFV      core_DTS     [I16V]   1.0   \n",
       "479233        NaN        68.0  VFVVVFV      core_DTS     [I16F]   1.0   \n",
       "479426        NaN        68.0  VIMVVFV      core_DTS     [V25M]   1.0   \n",
       "479427        NaN        68.0  VILVVFV      core_DTS     [V25L]   1.0   \n",
       "479428        NaN        68.0  VIIVVFV      core_DTS     [V25I]   1.0   \n",
       "479431        NaN        68.0  VIFVVFV      core_DTS     [V25F]   1.0   \n",
       "479470        NaN        68.0  VIVMVFV      core_DTS     [V27M]   1.0   \n",
       "479471        NaN        68.0  VIVLVFV      core_DTS     [V27L]   1.0   \n",
       "479472        NaN        68.0  VIVIVFV      core_DTS     [V27I]   1.0   \n",
       "479475        NaN        68.0  VIVFVFV      core_DTS     [V27F]   1.0   \n",
       "479888        NaN        68.0  VIVVMFV      core_DTS     [V46M]   1.0   \n",
       "479889        NaN        68.0  VIVVLFV      core_DTS     [V46L]   1.0   \n",
       "479890        NaN        68.0  VIVVIFV      core_DTS     [V46I]   1.0   \n",
       "479893        NaN        68.0  VIVVFFV      core_DTS     [V46F]   1.0   \n",
       "479932        NaN        68.0  VIVVVMV      core_DTS     [F48M]   1.0   \n",
       "479933        NaN        68.0  VIVVVLV      core_DTS     [F48L]   1.0   \n",
       "479934        NaN        68.0  VIVVVVV      core_DTS     [F48V]   1.0   \n",
       "479935        NaN        68.0  VIVVVIV      core_DTS     [F48I]   1.0   \n",
       "480240        NaN        68.0  VIVVVFM      core_DTS     [V62M]   1.0   \n",
       "480241        NaN        68.0  VIVVVFL      core_DTS     [V62L]   1.0   \n",
       "480242        NaN        68.0  VIVVVFI      core_DTS     [V62I]   1.0   \n",
       "480245        NaN        68.0  VIVVVFF      core_DTS     [V62F]   1.0   \n",
       "\n",
       "        redundant_single  \n",
       "478964             False  \n",
       "478965             False  \n",
       "478966             False  \n",
       "478969             False  \n",
       "479228             False  \n",
       "479229             False  \n",
       "479230             False  \n",
       "479233             False  \n",
       "479426             False  \n",
       "479427             False  \n",
       "479428             False  \n",
       "479431             False  \n",
       "479470             False  \n",
       "479471             False  \n",
       "479472             False  \n",
       "479475             False  \n",
       "479888             False  \n",
       "479889             False  \n",
       "479890             False  \n",
       "479893             False  \n",
       "479932             False  \n",
       "479933             False  \n",
       "479934             False  \n",
       "479935             False  \n",
       "480240             False  \n",
       "480241             False  \n",
       "480242             False  \n",
       "480245             False  \n",
       "\n",
       "[28 rows x 43 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DTS=['F','L','I','M','V']\n",
    "query_protein='P0A9X9'\n",
    "pdb='1MJC'\n",
    "core_positions = [i for i in cores[query_protein]]\n",
    "query_protein_wt = str(Seq(P0A9X9).translate())\n",
    "rocklin23_query_protein = rocklin23[rocklin23['name'].str.startswith(pdb)].copy()#.loc[rocklin['scramble']!=True].copy()\n",
    "for index, row in rocklin23_query_protein.iterrows():\n",
    "    #rocklin23_query_protein.at[index,'aa_seq']=str(Seq(row.dna_seq).translate())\n",
    "    rocklin23_query_protein.at[index,'len_aa_seq']=len(row.aa_seq)\n",
    "\n",
    "rocklin23_query_protein = rocklin23_query_protein.loc[rocklin23_query_protein['len_aa_seq']==68]\n",
    "for index, row in rocklin23_query_protein.iterrows():\n",
    "    variant=row.aa_seq[2:-1]\n",
    "    mutations=[]\n",
    "    core_mutant=[]\n",
    "    iterator=0\n",
    "    for wt_res, var_res in zip(query_protein_wt, variant):\n",
    "        if wt_res != var_res:\n",
    "            mutations.append(wt_res+str(iterator)+var_res)\n",
    "            if iterator in core_positions:\n",
    "                core_mutant.append(True)\n",
    "            else:\n",
    "                core_mutant.append(False)\n",
    "        iterator+=1\n",
    "    var_core=[]\n",
    "    for core_pos in core_positions:\n",
    "        var_core.append(variant[core_pos])\n",
    "    rocklin23_query_protein.at[index,'core']=''.join(var_core)\n",
    "    if all(i==True for i in core_mutant)==True and set(var_core) <= set(DTS) and len(mutations)>0:\n",
    "        rocklin23_query_protein.at[index,'mutant_class']='core_DTS'\n",
    "    elif all(i==True for i in core_mutant)==True and not set(var_core) <= set(DTS):\n",
    "        rocklin23_query_protein.at[index,'mutant_class']='core_non_DTS'\n",
    "    elif len(mutations)>0:\n",
    "        rocklin23_query_protein.at[index,'mutant_class']='non_core'\n",
    "    if len(mutations)>0:\n",
    "        rocklin23_query_protein.at[index,'mutations']=mutations\n",
    "    rocklin23_query_protein.at[index,'Nham']=len(mutations)\n",
    "    try:\n",
    "        if row['name'].split('_')[1][-1]+row['name'].split('_')[1][1:-1] == row['name'].split('_')[2][:-1]:\n",
    "            rocklin23_query_protein.at[index,'redundant_single']=True\n",
    "        else:\n",
    "            rocklin23_query_protein.at[index,'redundant_single']=False\n",
    "    except IndexError:\n",
    "        rocklin23_query_protein.at[index,'redundant_single']=False\n",
    "\n",
    "rocklin23_query_protein=rocklin23_query_protein.loc[rocklin23_query_protein['Nham']==1].loc[rocklin23_query_protein['redundant_single']!=True]\n",
    "rocklin23_query_protein.loc[rocklin23_query_protein['mutant_class']=='core_DTS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "92127c5a-df7a-439b-b433-65047cd6a1e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x120 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "protein_variants = allvariants.loc[allvariants['Protein']==query_protein].copy()\n",
    "\n",
    "# Normalize data in the -1 to 0 range\n",
    "reference=allvariants.loc[allvariants['Protein']==query_protein].sort_values(['Nham_aa','fitness'], ascending=[True,False]).iloc[0]['core']\n",
    "\n",
    "fig = plt.figure(figsize=(0,0))\n",
    "lines = kdeplot(data=protein_variants,x='fitness',linewidth=0).get_lines()\n",
    "for line in lines:\n",
    "    x, y = line.get_data()\n",
    "    mode=(x[np.argmax(y)])\n",
    "    ax.axvline(x[np.argmax(y)], ls='--')\n",
    "plt.close()\n",
    "    \n",
    "protein_variants_scaled = protein_variants.copy()    \n",
    "\n",
    "protein_variants_scaled['fitness']=normalize(list(protein_variants_scaled['fitness']),{'actual': {'lower': mode, 'upper': protein_variants_scaled.loc[protein_variants_scaled['core']==reference]['fitness'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "protein_variants_scaled['fitness1_uncorr']=normalize(list(protein_variants_scaled['fitness1_uncorr']),{'actual': {'lower': mode, 'upper': protein_variants_scaled.loc[protein_variants_scaled['core']==reference]['fitness1_uncorr'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "protein_variants_scaled['fitness2_uncorr']=normalize(list(protein_variants_scaled['fitness2_uncorr']),{'actual': {'lower': mode, 'upper': protein_variants_scaled.loc[protein_variants_scaled['core']==reference]['fitness2_uncorr'].iloc[0]}, 'desired': {'lower': -1, 'upper': 0}})\n",
    "\n",
    "\n",
    "df = protein_variants_scaled.loc[protein_variants_scaled['Nham_aa']==1][['core','fitness','sigma']].merge(rocklin23_query_protein.loc[rocklin23_query_protein['mutant_class']=='core_DTS'][['core','deltaG','deltaG_95CI']], left_on='core', right_on='core')\n",
    "df.set_index('core', inplace=True)\n",
    "\n",
    "g = sns.FacetGrid(data=df, aspect=2)\n",
    "g.map(plt.errorbar, 'fitness', 'deltaG', 'deltaG_95CI','sigma', fmt='o', elinewidth=0.5, capsize=2, capthick=0.5, markersize=2, color='#B1D9D8')\n",
    "#g.add_legend(bbox_to_anchor=(1.3,0.7), title='', fontsize=6)\n",
    "g.fig.set_size_inches(1,1.2)\n",
    "g.tick_params(axis='both', which='major', labelsize=6)\n",
    "\n",
    "x = df[['fitness']].to_numpy()\n",
    "y = df[['deltaG']].to_numpy()\n",
    "\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x, y)\n",
    "\n",
    "y_pred = model.predict(x)\n",
    "r2 = r2_score(y,y_pred)\n",
    "r,p = pearsonr(y.ravel(),y_pred.ravel())\n",
    "\n",
    "plt.plot(x, y_pred, color='Gray', linewidth=1, linestyle='--')\n",
    "plt.xlabel('Abundance fitness', fontsize=7)\n",
    "plt.ylabel('Tsuboyama et al.\\nΔG / kcal mol$^-$$^1$', fontsize=7)\n",
    "#plt.title('R = '+str(round(r,2)), fontsize=7,y=0.5, pad=0,loc='right')\n",
    "plt.annotate('R = '+str(round(r,2))+'\\np-val = '+'{:0.2e}'.format(p),xy=(-1.5,6.4),fontsize=7, annotation_clip=False)\n",
    "plt.annotate('n = '+str(len(x)), xy=(-1.3,5.5),fontsize=7, annotation_clip=False)\n",
    "plt.xticks([-1.5,-1,-0.5,0],[-1.5,-1,-0.5,0])\n",
    "plt.xlim(-1.5,0.2)\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig(query_protein+'_Tsuboyama.svg', dpi=300, transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "705abff7-41d4-4e39-825e-816811f5eaae",
   "metadata": {},
   "source": [
    "## Panel h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b24b9b3a-a791-4db9-8e39-d7bbdae39e15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x50 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"font.family\"] = \"Arial\"\n",
    "\n",
    "prot_names = {'P06241' : 'FYN-SH3',\n",
    "              'P01053' : 'CI-2A',\n",
    "              'P0A9X9' : 'CspA'}\n",
    "\n",
    "total_core_viabilities=total_core_viabilities.rename(index=prot_names)\n",
    "total_core_viabilities=total_core_viabilities.reindex(list(prot_names.values()))\n",
    "total_core_viabilities=total_core_viabilities.rename(columns={'variants_above_01':'Fitness > -0.10',\n",
    "                                                              'variants_above_025':'Fitness > -0.25',\n",
    "                                                              'variants_above_05':'Fitness > -0.50',\n",
    "                                                              'variants_below_05':'Fitness < -0.50'})\n",
    "\n",
    "total_core_viabilities_subtract = total_core_viabilities.copy()\n",
    "for index, row in total_core_viabilities_subtract.iterrows():\n",
    "    total_core_viabilities_subtract.at[index,'Fitness > -0.25'] = row['Fitness > -0.25'] - row['Fitness > -0.10']\n",
    "    total_core_viabilities_subtract.at[index,'Fitness > -0.50'] = row['Fitness > -0.50'] - row['Fitness > -0.25'] - row['Fitness > -0.10']\n",
    "    #total_core_viabilities_subtract.at[index,'Fitness < -0.50'] = row['Fitness < -0.50'] - row['Fitness > -0.50'] - row['Fitness > -0.25'] - row['Fitness > -0.10']\n",
    "    \n",
    "orig_cmap = plt.cm.coolwarm\n",
    "iterator_cmap+=1\n",
    "#midpoint=(-0.5 - np.min(protein_variants_scaled['fitness']))/ (np.max(protein_variants_scaled['fitness']) - np.min(protein_variants_scaled['fitness']))\n",
    "shifted_cmap = shiftedColorMap(orig_cmap, midpoint=0.9, name='shifted'+str(iterator_cmap))\n",
    "\n",
    "from sklearn.preprocessing import Normalizer\n",
    "\n",
    "fig, ax= plt.subplots(figsize=(3,0.5))\n",
    "\n",
    "\n",
    "total_core_viabilities_normalized=total_core_viabilities_subtract.iloc[:,1:].copy()\n",
    "total_core_viabilities_normalized.iloc[:,:] = Normalizer(norm='l1').fit_transform(total_core_viabilities_normalized)\n",
    "total_core_viabilities_normalized.plot.barh(stacked=True, cmap=shifted_cmap, width=0.8,ax=ax,legend=False)\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "\n",
    "ax.spines[['left','right']].set_visible(False)\n",
    "#ax.spines[['left','right']].set_visible(False)\n",
    "\n",
    "ax.set_yticks(ax.get_yticks(),labels=ax.get_yticklabels(),fontsize=8)\n",
    "\n",
    "ax_top = ax.twiny()\n",
    "ax_top.set_xticks(ax.get_xticks(),labels=ax.get_xticklabels(),fontsize=6)\n",
    "ax_top.spines[['left','right']].set_visible(False)\n",
    "ax_top.set_xlabel('Variant fraction', fontsize=7)\n",
    "ax_top.tick_params(axis=\"x\", bottom=False, top=True, labelbottom=False, labeltop=True)\n",
    "ax_top.set_xlim(0,0.25)\n",
    "ax_top.set_xticks(np.arange(0,0.3,0.05),labels=[round(i,2) for i in np.arange(0,0.3,0.05)],fontsize=6)\n",
    "ax.set_xticks(ax.get_xticks(),labels=[int(i*5**7) for i in list(ax.get_xticks())],fontsize=6)\n",
    "ax.set_xlabel('Variant count', fontsize=7)\n",
    "ax.tick_params(axis=\"x\", bottom=True, top=False, labelbottom=True, labeltop=False)\n",
    "ax.set_xlim(0,0.25)\n",
    "ax.set_xticks(np.arange(0,0.3,0.05),labels=[int(i*5**7) for i in np.arange(0,0.3,0.05)],fontsize=6)\n",
    "fig.legend(bbox_to_anchor=[0.85,-0.65], frameon=False, fontsize=6, ncol=2)\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "#plt.savefig('Sparse_core_mutant_viability_summary.svg', dpi=300, transparent=True, bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}